510 AI Team Lead jobs in Hyderabad

Purpose/Objective

The AI Lead pivotal leadership role is crucial for driving the build-out of the Defence AI organization and its offerings. This position is responsible for spearheading innovation and growth within the sector, nurturing talent, and positioning Adani Defence as a key player in Defence and Aerospace AI applications both locally and globally. The AI Lead will lead strategic initiatives to enhance technological capabilities, foster a culture of continuous improvement, and ensure the organization remains at the forefront of AI advancements in the defence industry.

Key Responsibilities of Role

Responsibilities:

• Build Business Pipeline: Engage with Defence and Aerospace stakeholders to shape offerings that are relevant and impactful. This involves understanding the needs and challenges of these sectors and developing AI solutions that address them effectively.
• Build Organization: Create and nurture best-in-class capabilities across critical defence AI domains. This includes recruiting top talent, fostering a collaborative and innovative work environment, and ensuring continuous development of skills and expertise within the team.
• Oversee Creation of Relevant & Scalable AI Products & Solutions: Lead the development of configurable and scalable AI products and solutions that meet the needs of Defence and Aerospace customers. Ensure these solutions are adaptable and can be customized to address specific requirements.
• Ensure Timely Delivery of Key Projects: Oversee the timely delivery of key projects, including research and development projects in collaboration with academia. This involves setting clear timelines, managing resources effectively, and ensuring high-quality outcomes.
• Strategic Planning & Partnership: Develop the overall business build strategy and execute mergers and acquisitions as required. This includes identifying strategic opportunities, forming partnerships, and driving initiatives that support the long-term growth and success of the organization.

* Qualifications and Experience

Educational Qualification:

• Armed services/ Intelligence services
• Background in Science & Engineering

Experiences:

• 15+ years experience
• Direct oversight of AI technology deployments in Defence context
• Experience in Cyber Security is desirable.

Purpose/Objective

The AI Lead pivotal leadership role is crucial for driving the build-out of the Defence AI organization and its offerings. This position is responsible for spearheading innovation and growth within the sector, nurturing talent, and positioning Adani Defence as a key player in Defence and Aerospace AI applications both locally and globally. The AI Lead will lead strategic initiatives to enhance technological capabilities, foster a culture of continuous improvement, and ensure the organization remains at the forefront of AI advancements in the defence industry.

Key Responsibilities of Role

Responsibilities:

• Build Business Pipeline: Engage with Defence and Aerospace stakeholders to shape offerings that are relevant and impactful. This involves understanding the needs and challenges of these sectors and developing AI solutions that address them effectively.
• Build Organization: Create and nurture best-in-class capabilities across critical defence AI domains. This includes recruiting top talent, fostering a collaborative and innovative work environment, and ensuring continuous development of skills and expertise within the team.
• Oversee Creation of Relevant & Scalable AI Products & Solutions: Lead the development of configurable and scalable AI products and solutions that meet the needs of Defence and Aerospace customers. Ensure these solutions are adaptable and can be customized to address specific requirements.
• Ensure Timely Delivery of Key Projects: Oversee the timely delivery of key projects, including research and development projects in collaboration with academia. This involves setting clear timelines, managing resources effectively, and ensuring high-quality outcomes.
• Strategic Planning & Partnership: Develop the overall business build strategy and execute mergers and acquisitions as required. This includes identifying strategic opportunities, forming partnerships, and driving initiatives that support the long-term growth and success of the organization.

Qualifications and Experience

Educational Qualification:

• Armed services/ Intelligence services
• Background in Science & Engineering

Experiences:

• 15+ years experience
• Direct oversight of AI technology deployments in Defence context
• Experience in Cyber Security is desirable.

Beetexting is a growth stage and VC-backed SaaS company headquartered in the USA with a cutting-edge development center in Hyderabad, India. We are on a mission to build next-gen software products that transform communication with AI. Our team thrives on innovation, passion, and a relentless pursuit of excellence. We are looking for code lovers who are excited to develop groundbreaking products and make a real impact in the market.

• We are entrepreneurs. We’re resourceful and positive 
• We are innovators. We obsess on helping our customer win. 
• We are visionaries. We think deeply about the industry and think way, way ahead. 
• We are beelievers. We’re humble. We believe in each other and support one another. 
• We are givers. We think of others first. 
• We are intentional. We plan.
• We are diligent. We measure outcomes and stay focused on the profitable. 
• We are gritty. We execute and we never give up. 

Position:  AI Lead

Location:   Hyderabad, Telangana, India

• Spearhead the end-to-end development, deployment, and continuous optimization of cutting-edge AI/ML systems—Generative AI, LLMs/MLLMs, RAG, autonomous agents, and custom MCP servers in production 
• Architect and implement scalable Retrieval-Augmented Generation pipelines, multi-modal transformer systems, and autonomous multi-agent frameworks (LangChain, AutoGPT, BabyAGI, NVIDIA NeMo Agents) 
• Lead deep-learning model design and delivery: ANN, CNN, RNN, LSTM/BiLSTM, attention-based Transformers (BERT, GPT, T5, Vision Transformers), GANs, VAEs, Diffusion models, and Mixture-of-Experts architectures 
• Define and enforce best practices for prompt engineering, fine-tuning (LlamaIndex, Hugging Face), quantization (INT4, GGUF), pruning, distillation, and advanced inference optimizations 
• Build and maintain robust MLOps pipelines: CI/CD (Git, Bitbucket, automated tests), model versioning (MLflow, Hugging Face Hub & LFS), Docker/GPU containerization, Kubernetes/OpenShift orchestration, Terraform infrastructure as code 
• Design high-performance data engineering workflows: ETL optimization (Pandas, NumPy, Spark), real-time streaming (Kafka, RabbitMQ), and caching strategies (Redis) 
• Ensure secure, compliant AI deployments by embedding Responsible AI principles (bias mitigation, transparency, privacy), governance frameworks, and regulatory controls. 
• Monitor emerging research (self-supervised pre-training, federated learning, synthetic data, LLMOps) and integrate top innovations into production 
• Collaborate closely with Product, DevOps, Data Engineering, and Security teams; mentor and guide engineers and data scientists in software engineering and model validation best practices 

• Education:  Master’s or PhD in AI/ML, Computer Science (AI/ML specialization), Data Science, Mathematics, or related field 
• Industry Experience:  15+ years in AI/ML roles, including 7–10 years hands-on in Generative AI and Deep Learning, plus 5+ years in senior or leadership positions 
• Hands-On Coding & Deployment:  Expert at architecting, writing, debugging, deploying, and optimizing production-grade AI/ML code in Python (primary), C++, R, Java/Spring Boot, with strong testing, code-review, and performance-profiling discipline 
• Deep Learning & Generative AI Frameworks:  Advanced usage of PyTorch, TensorFlow, ONNX, GGUF, Hugging Face Transformers & Diffusers; fine-tuning pipelines (LlamaIndex, custom scripts) 
• Large Language & Multimodal Models:  Production deployment of LLMs, Multimodal LLMs (vision-language models, cross-modal understanding), RAG systems, and custom MCP A2A servers; embedding management & vector search with Pinecone, Milvus, Chroma, Quadrant 
• Advanced Model Optimization:  Quantization, pruning, distillation, MoE routing, efficient inference strategies, prompt-engineering platforms (LangSmith, PromptFlow) 
• Classical & Advanced ML:  Proficiency in regression, decision trees, random forests, SVM; boosting (XGBoost, CatBoost, LightGBM); clustering (K-Means, DBSCAN, hierarchical); RL (Q-Learning, DDPG, PPO); statistical methods and optimization math 
• Computer Vision & Document Processing:  Expertise in OpenCV, PyMuPDF, python-docx/pptx, pytesseract for advanced text/image extraction and analysis 
• Cloud & Infrastructure:  GPU acceleration (CUDA, cuDNN); AWS (Bedrock, SageMaker), Azure AI Studio & GPU Containers, GCP (Vertex AI, Cloud Run), serverless AI, NVIDIA Triton Inference Server 
• Backend & APIs:  FastAPI, OpenAPI, Django REST Framework, Flask for building and scaling AI microservices 
• Data Engineering & Storage:  ETL pipelines, streaming, caching; relational (MySQL, Oracle SQL), NoSQL (MongoDB), vector DBs 
• MLOps & Observability:  MLflow, Kubeflow; Docker, Kubernetes, OpenShift; CI/CD (Git, Bitbucket); monitoring with Prometheus, Grafana, OpenTelemetry, Logstash, Kibana 
• Security & Compliance:  Secure coding, network security, and adherence to GDPR, HIPAA, GxP standards 
• Industry Certifications & Research:  Must hold at least one recognized AI/ML certification (e.g. AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate) and have published research papers in top-tier AI/ML conferences or journals (NeurIPS, ACM, arXiv, AAAI, JMLR). 

• PhD-level publications and advanced certifications (Stanford AI, MIT, AWS, Google) 
• Experience with federated learning, synthetic data generation, privacy-preserving ML (differential privacy, homomorphic encryption), and edge/in-device inference (TensorFlow Lite, CoreML) 
• Familiarity with Responsible AI toolkits, model cards/DataCard’s, and AI risk management frameworks 
• Significant open-source contributions to major AI/ML projects 
• Proven success integrating AI in regulated industries (Texting/Messaging, pharma, healthcare, finance) Industries and disciplines.

If you are a passionate developer who loves coding and thrives in an innovative startup environment, apply now to join our journey in revolutionizing communication! 

Apply here or email to 

Beetexting is a growth stage and VC-backed SaaS company headquartered in the USA with a cutting-edge development center in Hyderabad, India. We are on a mission to build next-gen software products that transform communication with AI. Our team thrives on innovation, passion, and a relentless pursuit of excellence. We are looking for code lovers who are excited to develop groundbreaking products and make a real impact in the market.

• We are entrepreneurs. We’re resourceful and positive 
• We are innovators. We obsess on helping our customer win. 
• We are visionaries. We think deeply about the industry and think way, way ahead. 
• We are beelievers. We’re humble. We believe in each other and support one another. 
• We are givers. We think of others first. 
• We are intentional. We plan.
• We are diligent. We measure outcomes and stay focused on the profitable. 
• We are gritty. We execute and we never give up. 

Position:  AI Lead

Location:  Hyderabad, Telangana, India

• Spearhead the end-to-end development, deployment, and continuous optimization of cutting-edge AI/ML systems—Generative AI, LLMs/MLLMs, RAG, autonomous agents, and custom MCP servers in production 
• Architect and implement scalable Retrieval-Augmented Generation pipelines, multi-modal transformer systems, and autonomous multi-agent frameworks (LangChain, AutoGPT, BabyAGI, NVIDIA NeMo Agents) 
• Lead deep-learning model design and delivery: ANN, CNN, RNN, LSTM/BiLSTM, attention-based Transformers (BERT, GPT, T5, Vision Transformers), GANs, VAEs, Diffusion models, and Mixture-of-Experts architectures 
• Define and enforce best practices for prompt engineering, fine-tuning (LlamaIndex, Hugging Face), quantization (INT4, GGUF), pruning, distillation, and advanced inference optimizations 
• Build and maintain robust MLOps pipelines: CI/CD (Git, Bitbucket, automated tests), model versioning (MLflow, Hugging Face Hub & LFS), Docker/GPU containerization, Kubernetes/OpenShift orchestration, Terraform infrastructure as code 
• Design high-performance data engineering workflows: ETL optimization (Pandas, NumPy, Spark), real-time streaming (Kafka, RabbitMQ), and caching strategies (Redis) 
• Ensure secure, compliant AI deployments by embedding Responsible AI principles (bias mitigation, transparency, privacy), governance frameworks, and regulatory controls. 
• Monitor emerging research (self-supervised pre-training, federated learning, synthetic data, LLMOps) and integrate top innovations into production 
• Collaborate closely with Product, DevOps, Data Engineering, and Security teams; mentor and guide engineers and data scientists in software engineering and model validation best practices 

• Education:  Master’s or PhD in AI/ML, Computer Science (AI/ML specialization), Data Science, Mathematics, or related field 
• Industry Experience:  15+ years in AI/ML roles, including 7–10 years hands-on in Generative AI and Deep Learning, plus 5+ years in senior or leadership positions 
• Hands-On Coding & Deployment:  Expert at architecting, writing, debugging, deploying, and optimizing production-grade AI/ML code in Python (primary), C++, R, Java/Spring Boot, with strong testing, code-review, and performance-profiling discipline 
• Deep Learning & Generative AI Frameworks:  Advanced usage of PyTorch, TensorFlow, ONNX, GGUF, Hugging Face Transformers & Diffusers; fine-tuning pipelines (LlamaIndex, custom scripts) 
• Large Language & Multimodal Models:  Production deployment of LLMs, Multimodal LLMs (vision-language models, cross-modal understanding), RAG systems, and custom MCP A2A servers; embedding management & vector search with Pinecone, Milvus, Chroma, Quadrant 
• Advanced Model Optimization:  Quantization, pruning, distillation, MoE routing, efficient inference strategies, prompt-engineering platforms (LangSmith, PromptFlow) 
• Classical & Advanced ML:  Proficiency in regression, decision trees, random forests, SVM; boosting (XGBoost, CatBoost, LightGBM); clustering (K-Means, DBSCAN, hierarchical); RL (Q-Learning, DDPG, PPO); statistical methods and optimization math 
• Computer Vision & Document Processing:  Expertise in OpenCV, PyMuPDF, python-docx/pptx, pytesseract for advanced text/image extraction and analysis 
• Cloud & Infrastructure:  GPU acceleration (CUDA, cuDNN); AWS (Bedrock, SageMaker), Azure AI Studio & GPU Containers, GCP (Vertex AI, Cloud Run), serverless AI, NVIDIA Triton Inference Server 
• Backend & APIs:  FastAPI, OpenAPI, Django REST Framework, Flask for building and scaling AI microservices 
• Data Engineering & Storage:  ETL pipelines, streaming, caching; relational (MySQL, Oracle SQL), NoSQL (MongoDB), vector DBs 
• MLOps & Observability:  MLflow, Kubeflow; Docker, Kubernetes, OpenShift; CI/CD (Git, Bitbucket); monitoring with Prometheus, Grafana, OpenTelemetry, Logstash, Kibana 
• Security & Compliance:  Secure coding, network security, and adherence to GDPR, HIPAA, GxP standards 
• Industry Certifications & Research:  Must hold at least one recognized AI/ML certification (e.g. AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate) and have published research papers in top-tier AI/ML conferences or journals (NeurIPS, ACM, arXiv, AAAI, JMLR). 

• PhD-level publications and advanced certifications (Stanford AI, MIT, AWS, Google) 
• Experience with federated learning, synthetic data generation, privacy-preserving ML (differential privacy, homomorphic encryption), and edge/in-device inference (TensorFlow Lite, CoreML) 
• Familiarity with Responsible AI toolkits, model cards/DataCard’s, and AI risk management frameworks 
• Significant open-source contributions to major AI/ML projects 
• Proven success integrating AI in regulated industries (Texting/Messaging, pharma, healthcare, finance) Industries and disciplines.

If you are a passionate developer who loves coding and thrives in an innovative startup environment, apply now to join our journey in revolutionizing communication! 

Apply here or email to 

Purpose/Objective

The AI Lead pivotal leadership role is crucial for driving the build-out of the Defence AI organization and its offerings. This position is responsible for spearheading innovation and growth within the sector, nurturing talent, and positioning Adani Defence as a key player in Defence and Aerospace AI applications both locally and globally. The AI Lead will lead strategic initiatives to enhance technological capabilities, foster a culture of continuous improvement, and ensure the organization remains at the forefront of AI advancements in the defence industry.

Key Responsibilities of Role

Responsibilities:

• Build Business Pipeline: Engage with Defence and Aerospace stakeholders to shape offerings that are relevant and impactful. This involves understanding the needs and challenges of these sectors and developing AI solutions that address them effectively.
• Build Organization: Create and nurture best-in-class capabilities across critical defence AI domains. This includes recruiting top talent, fostering a collaborative and innovative work environment, and ensuring continuous development of skills and expertise within the team.
• Oversee Creation of Relevant & Scalable AI Products & Solutions: Lead the development of configurable and scalable AI products and solutions that meet the needs of Defence and Aerospace customers. Ensure these solutions are adaptable and can be customized to address specific requirements.
• Ensure Timely Delivery of Key Projects: Oversee the timely delivery of key projects, including research and development projects in collaboration with academia. This involves setting clear timelines, managing resources effectively, and ensuring high-quality outcomes.
• Strategic Planning & Partnership: Develop the overall business build strategy and execute mergers and acquisitions as required. This includes identifying strategic opportunities, forming partnerships, and driving initiatives that support the long-term growth and success of the organization.

* Qualifications and Experience

Educational Qualification:

• Armed services/ Intelligence services
• Background in Science & Engineering

Experiences:

• 15+ years experience
• Direct oversight of AI technology deployments in Defence context
• Experience in Cyber Security is desirable.

Purpose/Objective

The AI Lead pivotal leadership role is crucial for driving the build-out of the Defence AI organization and its offerings. This position is responsible for spearheading innovation and growth within the sector, nurturing talent, and positioning Adani Defence as a key player in Defence and Aerospace AI applications both locally and globally. The AI Lead will lead strategic initiatives to enhance technological capabilities, foster a culture of continuous improvement, and ensure the organization remains at the forefront of AI advancements in the defence industry.

Key Responsibilities of Role

Responsibilities:

• Build Business Pipeline: Engage with Defence and Aerospace stakeholders to shape offerings that are relevant and impactful. This involves understanding the needs and challenges of these sectors and developing AI solutions that address them effectively.
• Build Organization: Create and nurture best-in-class capabilities across critical defence AI domains. This includes recruiting top talent, fostering a collaborative and innovative work environment, and ensuring continuous development of skills and expertise within the team.
• Oversee Creation of Relevant & Scalable AI Products & Solutions: Lead the development of configurable and scalable AI products and solutions that meet the needs of Defence and Aerospace customers. Ensure these solutions are adaptable and can be customized to address specific requirements.
• Ensure Timely Delivery of Key Projects: Oversee the timely delivery of key projects, including research and development projects in collaboration with academia. This involves setting clear timelines, managing resources effectively, and ensuring high-quality outcomes.
• Strategic Planning & Partnership: Develop the overall business build strategy and execute mergers and acquisitions as required. This includes identifying strategic opportunities, forming partnerships, and driving initiatives that support the long-term growth and success of the organization.

* Qualifications and Experience

Educational Qualification:

• Armed services/ Intelligence services
• Background in Science & Engineering

Experiences:

• 15+ years experience
• Direct oversight of AI technology deployments in Defence context
• Experience in Cyber Security is desirable.

Beetexting is a growth stage and VC-backed SaaS company headquartered in the USA with a cutting-edge development center in Hyderabad, India. We are on a mission to build next-gen software products that transform communication with AI. Our team thrives on innovation, passion, and a relentless pursuit of excellence. We are looking for code lovers who are excited to develop groundbreaking products and make a real impact in the market.

• We are entrepreneurs. We’re resourceful and positive
• We are innovators. We obsess on helping our customer win.
• We are visionaries. We think deeply about the industry and think way, way ahead.
• We are beelievers. We’re humble. We believe in each other and support one another.
• We are givers. We think of others first.
• We are intentional. We plan.
• We are diligent. We measure outcomes and stay focused on the profitable.
• We are gritty. We execute and we never give up.

Position: AI Lead

Location: Hyderabad, Telangana, India

• Spearhead the end-to-end development, deployment, and continuous optimization of cutting-edge AI/ML systems—Generative AI, LLMs/MLLMs, RAG, autonomous agents, and custom MCP servers in production
• Architect and implement scalable Retrieval-Augmented Generation pipelines, multi-modal transformer systems, and autonomous multi-agent frameworks (LangChain, AutoGPT, BabyAGI, NVIDIA NeMo Agents)
• Lead deep-learning model design and delivery: ANN, CNN, RNN, LSTM/BiLSTM, attention-based Transformers (BERT, GPT, T5, Vision Transformers), GANs, VAEs, Diffusion models, and Mixture-of-Experts architectures
• Define and enforce best practices for prompt engineering, fine-tuning (LlamaIndex, Hugging Face), quantization (INT4, GGUF), pruning, distillation, and advanced inference optimizations
• Build and maintain robust MLOps pipelines: CI/CD (Git, Bitbucket, automated tests), model versioning (MLflow, Hugging Face Hub & LFS), Docker/GPU containerization, Kubernetes/OpenShift orchestration, Terraform infrastructure as code
• Design high-performance data engineering workflows: ETL optimization (Pandas, NumPy, Spark), real-time streaming (Kafka, RabbitMQ), and caching strategies (Redis)
• Ensure secure, compliant AI deployments by embedding Responsible AI principles (bias mitigation, transparency, privacy), governance frameworks, and regulatory controls.
• Monitor emerging research (self-supervised pre-training, federated learning, synthetic data, LLMOps) and integrate top innovations into production
• Collaborate closely with Product, DevOps, Data Engineering, and Security teams;
  mentor and guide engineers and data scientists in software engineering and model validation best practices

• Education: Master’s or PhD in AI/ML, Computer Science (AI/ML specialization), Data Science, Mathematics, or related field
• Industry Experience: 15+ years in AI/ML roles, including 7–10 years hands-on in Generative AI and Deep Learning, plus 5+ years in senior or leadership positions
• Hands-On Coding & Deployment: Expert at architecting, writing, debugging, deploying, and optimizing production-grade AI/ML code in Python (primary), C++, R, Java/Spring Boot, with strong testing, code-review, and performance-profiling discipline
• Deep Learning & Generative AI Frameworks: Advanced usage of PyTorch, TensorFlow, ONNX, GGUF, Hugging Face Transformers & Diffusers;
  fine-tuning pipelines (LlamaIndex, custom scripts)
• Large Language & Multimodal Models: Production deployment of LLMs, Multimodal LLMs (vision-language models, cross-modal understanding), RAG systems, and custom MCP A2A servers;
  embedding management & vector search with Pinecone, Milvus, Chroma, Quadrant
• Advanced Model Optimization: Quantization, pruning, distillation, MoE routing, efficient inference strategies, prompt-engineering platforms (LangSmith, PromptFlow)
• Classical & Advanced ML: Proficiency in regression, decision trees, random forests, SVM;
  boosting (XGBoost, CatBoost, LightGBM);
  clustering (K-Means, DBSCAN, hierarchical);
  RL (Q-Learning, DDPG, PPO);
  statistical methods andoptimization math
• Computer Vision & Document Processing: Expertise in OpenCV, PyMuPDF, python-docx/pptx, pytesseract for advanced text/image extraction and analysis
• Cloud & Infrastructure: GPU acceleration (CUDA, cuDNN);
  AWS (Bedrock, SageMaker), Azure AI Studio & GPU Containers, GCP (Vertex AI, Cloud Run), serverless AI, NVIDIA Triton Inference Server
• Backend & APIs: FastAPI, OpenAPI, Django REST Framework, Flask for building and scaling AI microservices
• Data Engineering & Storage: ETL pipelines, streaming, caching;
  relational (MySQL, Oracle SQL), NoSQL (MongoDB), vector DBs
• MLOps & Observability: MLflow, Kubeflow;
  Docker, Kubernetes, OpenShift;
  CI/CD (Git, Bitbucket);
  monitoring with Prometheus, Grafana, OpenTelemetry, Logstash, Kibana
• Security & Compliance: Secure coding, network security, and adherence to GDPR, HIPAA, GxP standards
• Industry Certifications & Research: Must hold at least one recognized AI/ML certification (e.G. AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate) and have published research papers in top-tier AI/ML conferences or journals (NeurIPS, ACM, arXiv, AAAI, JMLR).

• PhD-level publications and advanced certifications (Stanford AI, MIT, AWS, Google)
• Experience with federated learning, synthetic data generation, privacy-preserving ML (differential privacy, homomorphic encryption), and edge/in-device inference (TensorFlow Lite, CoreML)
• Familiarity with Responsible AI toolkits, model cards/DataCard’s, and AI risk management frameworks
• Significant open-source contributions to major AI/ML projects
• Proven success integrating AI in regulated industries (Texting/Messaging, pharma, healthcare, finance) Industries and disciplines.

If you are a passionate developer who loves coding and thrives in an innovative startup environment, apply now to join our journey in revolutionizing communication!

Apply here or email to