1450 AI Specialists jobs in Hyderabad

We are seeking a highly skilled and innovative AI Lead/Developer with proven experience in designing, developing, and deploying AI Agents and Conversational AI Chatbots using Azure Cloud Services . You will play a critical role in transforming enterprise workflows through intelligent automation, integrating with modern AI platforms, and delivering scalable solutions that drive business outcomes.

Required Skills & Experience:

 Preferred Qualifications:

Job Summary:

We are seeking a hands-on AI Engineer to design, build, and deploy intelligent AI agents using GCP Vertex AI , LangChain , and modern UI tools like Streamlit . The ideal candidate will bring together skills in large language models (LLMs), agent orchestration , MLOps , and user-friendly interface development to create powerful and accessible AI solutions.

• Key Responsibilities: Design and implement LLM-based agents using LangChain , integrated with GCP Vertex AI services.
• Build interactive UIs using Streamlit or similar frameworks to showcase and test AI agent capabilities.
• Develop end-to-end ML pipelines for training, evaluation, and deployment using tools like Vertex Pipelines , Kubeflow , or Airflow .
• Integrate with APIs, vector databases, and knowledge sources to enable RAG (Retrieval-Augmented Generation) workflows.
• Deploy scalable, secure AI services using CI/CD pipelines , infrastructure-as-code, and version-controlled model registries.
• Monitor model performance, manage experiments, and optimize agent behavior in production environments.
• Work cross-functionally with product, design, and engineering teams to deliver intuitive, high-impact AI-powered applications.

• Required Qualifications: Relevant of 3–6 years of hands-on experience in AI/ML engineering , including recent work with LLMs and LangChain .
• Proficiency with GCP Vertex AI tools such as Pipelines, Model Registry, Training, and Endpoints.
• Strong Python programming skills , with experience in FastAPI , Flask , or similar web frameworks.
• Demonstrated experience building interactive dashboards or tools using Streamlit , Gradio , or Dash .
• Knowledge of MLOps workflows , including tools like MLflow , Weights & Biases , or Vertex AI Experiments .
• Experience working with vector stores (e.g., FAISS, Pinecone, Weaviate) in agent pipelines.
• Familiarity with retrieval-based QA , embeddings, and prompt engineering techniques.
• Experience with LangGraph or similar agent orchestration frameworks.

• Preferred Qualifications: Familiarity with cloud-native deployment and DevOps tools (Terraform, Docker, GCP Cloud Build).
• Background in UX/UI design thinking or rapid prototyping for AI-driven applications.
• Experience integrating LLMs with external APIs or private knowledge sources.

Job Summary:

We are seeking a hands-on AI Engineer to design, build, and deploy intelligent AI agents using GCP Vertex AI , LangChain , and modern UI tools like Streamlit . The ideal candidate will bring together skills in large language models (LLMs), agent orchestration , MLOps , and user-friendly interface development to create powerful and accessible AI solutions.

• Key Responsibilities: Design and implement LLM-based agents using LangChain , integrated with GCP Vertex AI services.
• Build interactive UIs using Streamlit or similar frameworks to showcase and test AI agent capabilities.
• Develop end-to-end ML pipelines for training, evaluation, and deployment using tools like Vertex Pipelines , Kubeflow , or Airflow .
• Integrate with APIs, vector databases, and knowledge sources to enable RAG (Retrieval-Augmented Generation) workflows.
• Deploy scalable, secure AI services using CI/CD pipelines , infrastructure-as-code, and version-controlled model registries.
• Monitor model performance, manage experiments, and optimize agent behavior in production environments.
• Work cross-functionally with product, design, and engineering teams to deliver intuitive, high-impact AI-powered applications.

• Required Qualifications: Relevant of 3–6 years of hands-on experience in AI/ML engineering , including recent work with LLMs and LangChain .
• Proficiency with GCP Vertex AI tools such as Pipelines, Model Registry, Training, and Endpoints.
• Strong Python programming skills , with experience in FastAPI , Flask , or similar web frameworks.
• Demonstrated experience building interactive dashboards or tools using Streamlit , Gradio , or Dash .
• Knowledge of MLOps workflows , including tools like MLflow , Weights & Biases , or Vertex AI Experiments .
• Experience working with vector stores (e.g., FAISS, Pinecone, Weaviate) in agent pipelines.
• Familiarity with retrieval-based QA , embeddings, and prompt engineering techniques.
• Experience with LangGraph or similar agent orchestration frameworks.

• Preferred Qualifications: Familiarity with cloud-native deployment and DevOps tools (Terraform, Docker, GCP Cloud Build).
• Background in UX/UI design thinking or rapid prototyping for AI-driven applications.
• Experience integrating LLMs with external APIs or private knowledge sources.

TL;DR : This role blends deep, publishable AI research with hands-on system development , ensuring you can conduct research, build, validate, and ship groundbreaking AI capabilities end‑to‑end.

You're a bridge between science and software. You’ll innovate on LLMs, prompt systems, evaluation frameworks, and algorithm pipelines—and bring them to production-grade systems that power real users. Expect to author experimental prototypes rich enough for academic-quality write-ups, then productize those same ideas into robust, reliable software.

Research Prototyping

• Tackle experiments in LLM training, LLM agent architectures, fine tuning, RL, and new evaluation methodologies.
• Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.

Model & Architecture Innovation

• Develop and benchmark improvements in LLM architecture, fine‑tuning workflows, and generation control.
• Optimize for performance, resource efficiency, and interpretability.

Production Engineering

• Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
• Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.

Scientific Experiment Tooling

• Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
• Enable internal teams to run reproducible experiments at scale.

Knowledge Sharing & Publication

• Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
• Participate in code and science critique sessions with peers.

Master’s or Ph.D. in CS, ML, or related (or equivalent experience)

• Deep familiarity with LLMs, fine‑tuning, reinforcment learning.
• Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs
• Strong software engineering intuition: modular design, CI/CD, containerization, automated testing
• Demonstrated track record in both research and production settings (e.g., prototypes published internally or externally)
• Bonus: familiarity with Kubernetes, JAX compiler stacks (e.g. Flax, Alpa), Hugging Face Accelerate, or MLOps toolchains

Research Innovation

Prototypes demonstrating clear scientific advances (e.g. better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.

Engineering Quality & Velocity

Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.

System Impact & Use

R&D code drives real product features—high user adoption, improved reliability or performance.

TL;DR : This role blends deep, publishable AI research with hands-on system development , ensuring you can conduct research, build, validate, and ship groundbreaking AI capabilities end‑to‑end.

You're a bridge between science and software. You’ll innovate on LLMs, prompt systems, evaluation frameworks, and algorithm pipelines—and bring them to production-grade systems that power real users. Expect to author experimental prototypes rich enough for academic-quality write-ups, then productize those same ideas into robust, reliable software.

Research Prototyping

• Tackle experiments in LLM training, LLM agent architectures, fine tuning, RL, and new evaluation methodologies.
• Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.

Model & Architecture Innovation

• Develop and benchmark improvements in LLM architecture, fine‑tuning workflows, and generation control.
• Optimize for performance, resource efficiency, and interpretability.

Production Engineering

• Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
• Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.

Scientific Experiment Tooling

• Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
• Enable internal teams to run reproducible experiments at scale.

Knowledge Sharing & Publication

• Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
• Participate in code and science critique sessions with peers.

Master’s or Ph.D. in CS, ML, or related (or equivalent experience)

• Deep familiarity with LLMs, fine‑tuning, reinforcment learning.
• Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs
• Strong software engineering intuition: modular design, CI/CD, containerization, automated testing
• Demonstrated track record in both research and production settings (e.g., prototypes published internally or externally)
• Bonus: familiarity with Kubernetes, JAX compiler stacks (e.g. Flax, Alpa), Hugging Face Accelerate, or MLOps toolchains

Research Innovation

Prototypes demonstrating clear scientific advances (e.g. better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.

Engineering Quality & Velocity

Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.

System Impact & Use

R&D code drives real product features—high user adoption, improved reliability or performance.

TL;DR : This role blends deep, publishable AI research with hands-on system development , ensuring you can conduct research, build, validate, and ship groundbreaking AI capabilities end‑to‑end.

You're a bridge between science and software. You’ll innovate on LLMs, prompt systems, evaluation frameworks, and algorithm pipelines—and bring them to production-grade systems that power real users. Expect to author experimental prototypes rich enough for academic-quality write-ups, then productize those same ideas into robust, reliable software.

Research Prototyping

• Tackle experiments in LLM training, LLM agent architectures, fine tuning, RL, and new evaluation methodologies.
• Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.

Model & Architecture Innovation

• Develop and benchmark improvements in LLM architecture, fine‑tuning workflows, and generation control.
• Optimize for performance, resource efficiency, and interpretability.

Production Engineering

• Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
• Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.

Scientific Experiment Tooling

• Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
• Enable internal teams to run reproducible experiments at scale.

Knowledge Sharing & Publication

• Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
• Participate in code and science critique sessions with peers.

Master’s or Ph.D. in CS, ML, or related (or equivalent experience)

• Deep familiarity with LLMs, fine‑tuning, reinforcment learning.
• Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs
• Strong software engineering intuition: modular design, CI/CD, containerization, automated testing
• Demonstrated track record in both research and production settings (e.g., prototypes published internally or externally)
• Bonus: familiarity with Kubernetes, JAX compiler stacks (e.g. Flax, Alpa), Hugging Face Accelerate, or MLOps toolchains

Research Innovation

Prototypes demonstrating clear scientific advances (e.g. better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.

Engineering Quality & Velocity

Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.

System Impact & Use

R&D code drives real product features—high user adoption, improved reliability or performance.