22 Machine Learning Techniques jobs in Chennai

Greetings from Colan Infotech!

Role - Data Scientist

Experience - 6+ Years

Job Location - Chennai/Bangalore

Notice Period - Immediate to 30 Days

Primary Skills Needed   : AI/ML, Tensorflow, Django, Pytorch, NLP, Image processing,Gen AI,LLM

Secondary Skills Needed   : Keras, OpenCV, Azure or AWS

Job Description:-

• Practical knowledge and working experience on Statistics and Operation Research methods.
• Practical knowledge and working experience in tools and frameworks like Flask, PySpark, Pytorch, tensorflow, keras, Databricks, OpenCV, Pillow/PIL, streamlit, d3js, dashplotly, neo4j.
• Good understanding of how to apply predictive and machine learning techniques like regression models, XGBoost, random forest, GBM, Neural Nets, SVM etc.
• Proficient with NLP techniques like RNN, LSTM and Attention based models and effectively handle readily available stanford, IBM, Azure, Open AI NLP models.
• Good understanding of SQL from a perspective of how to write efficient queries for pulling the data from database.
• Hands on experience on any version control tool (github, bitbucket). Experience of deploying ML models into production environment experience (MLOps) in any one of the cloud platforms like Azure and AWS
• Comprehend business issues and propose valuable business solutions.
• Design Factual or AI/profound learning models to address business issues.
• Design Statistical Models/ML/DL models and deploy them for production.
• Formulate what information is accessible from where and how to augment it.
• Develop innovative graphs for data comprehension using d3js, dashplotly and neo4j

Interested candidates send updated resume to

Director Data Science – (AI/ML, GenAI & BFSI Focus)

From vision to enterprise impact — lead the future of AI innovation.

Location: Chennai, India

Experience: 13–15 years in Data Science/AI/ML (with strong BFSI or other data-rich industry expertise) We’re seeking trailblazers who go beyond building models — leaders who drive AI strategy, GenAI adoption, agentic automation, and enterprise-scale transformation with measurable business outcomes.

Role Overview

As a Principal Data Scientist, you will set the direction for AI/ML innovation, embedding Generative AI and Agentic AI systems into BFSI solutions that transform how enterprises operate. You’ll collaborate with executive stakeholders, mentor high-performing teams, and translate cutting-edge research into enterprise-ready AI platforms that deliver measurable ROI.

What makes you right for the role?

• Strategic AI & GenAI Leadership – Define and drive enterprise AI/ML roadmaps, including adoption of LLMs, multi-agent systems, and AI copilots aligned to business priorities.
• Agentic AI & Automation – Architect and deploy agentic workflows for decisioning, customer engagement, fraud detection, and operational intelligence.
• Advanced Modeling & GenAI Expertise – Lead development of scalable ML and GenAI solutions across fraud detection, customer intelligence, risk modeling, hyper-personalization, and intelligent document processing.
• Productization at Scale – Translate AI/GenAI research into robust, production-grade systems , ensuring security, compliance, and business value.
• Responsible AI & Governance – Partner with senior stakeholders to design responsible, explainable AI frameworks ensuring fairness, compliance, and trust.
• Thought Leadership – Represent Crayon in client workshops, industry forums, and innovation showcases, shaping the narrative of applied AI and Agentic in BFSI.
• Team & Innovation Leadership – Mentor senior data scientists, lead cross-functional teams, and champion best practices in MLOps, GenAIOps, and applied AI innovation.

The person you are

• Advanced degree (Master’s/PhD) in Machine Learning, AI, Computer Science, Applied Math, or Statistics.
• 11–13 years of proven experience applying ML/AI at scale, ideally in BFSI or similar data-rich, regulated industries.
• Deep expertise in Python, ML/GenAI frameworks (TensorFlow, PyTorch, Hugging Face, LangChain, LlamaIndex), and cloud platforms (AWS, Azure, GCP).
• Strong track record in AI strategy, GenAI adoption, and agentic automation with measurable ROI.
• A visionary mindset with passion for experimentation, innovation, and building next-gen AI ecosystems.
• Chennai-based, with flexibility to travel for client and global team collaborations.

Job Title: Trainer – AI/ML/Data Science (Data Focus)

Job Location: Chennai (MEPZ – Tambaram)

Experience: 4–5 Years

Work Mode: Full-Time, Work from Office

Job Description:

We are seeking a passionate and experienced Trainer in AI/ML/Data Science with a strong focus on data engineering and big data technologies . The ideal candidate will have hands-on experience in delivering technical training and designing engaging learning content.

Key Responsibilities:

• Deliver high-quality training sessions on AI, ML, and Data Science with emphasis on data engineering and big data tools.
• Design and develop training materials including presentations, labs, assessments, and case studies.
• Customize training programs based on audience profiles and industry needs.
• Conduct both classroom and virtual training sessions.
• Keep content updated with the latest industry trends and technologies.
• Evaluate learner performance and provide constructive feedback.
• Collaborate in curriculum development and training effectiveness reviews.

Required Skills & Experience:

• 4–5 years of experience in AI/ML/Data Science with exposure to big data tools.
• Proficient in Python and libraries like NumPy, Pandas, scikit-learn.
• Hands-on experience with big data platforms: Hadoop, Spark, Hive, Kafka.
• Strong understanding of data pipelines, preprocessing, and feature engineering.
• Experience in training lifecycle: content creation, delivery, and assessment.
• Excellent communication and presentation skills.
• Ability to simplify complex concepts for diverse audiences.

Preferred Qualifications:

• Bachelor’s/Master’s/PhD in Data Science, Computer Science, or related fields.
• Certifications in AI/ML/Data Engineering from reputed platforms.
• Prior experience in corporate or academic training.
• Familiarity with cloud platforms like AWS, GCP, or Azure.

Position Summary:

Knorr-Bremse, the world leader in braking systems and safety-critical mobility solutions, is building an AI Center of Excellence to accelerate innovation across truck and rail businesses.

We are seeking a skilled ML/GenAI Engineer to be the operational backbone of our AI team. Your mission is to take the innovative models and prototypes developed by our scientists and transform them into production-grade, scalable, and highly efficient services.

This is a hands-on engineering role focused on the post-modeling lifecycle: optimization, deployment, and MLOps. You will be the expert on making our cutting-edge Generative AI systems (including LLMs and RAG pipelines) run fast, consume minimal resources, and operate reliably at scale. You will bridge the critical gap between experimental AI and robust, enterprise-ready applications.

If you are passionate about the engineering challenges of making large models performant and cost-effective in real-world environments, this role is for you.

Essential Functions:

• Model Optimization and Inference Acceleration: Analyze and optimize inference scripts for low latency and high throughput. Implement advanced techniques to reduce model memory consumption and computational cost, such as quantization (e.g., bitsandbytes, GPTQ), pruning, and knowledge distillation. Leverage specialized serving runtimes and compilers (e.g., vLLM, TensorRT-LLM, Triton Inference Server) to maximize hardware utilization.

• Deployment and Infrastructure Management: Containerize AI applications and their dependencies using Docker for reproducible deployments. Deploy models as scalable and resilient microservices on cloud platforms (AWS, GCP and/or Azure) using services like Kubernetes (GKE/AKS), Cloud Run, or Vertex AI Endpoints. Build and manage the cloud infrastructure required to support our AI services, using Infrastructure as Code (IaC) principles where applicable (e.g., Terraform).

• MLOps and Automation: Design and implement robust CI/CD pipelines for automated model testing, building, and deployment. Develop comprehensive monitoring and logging solutions to track model performance, resource usage, and system health in production. Implement alerting mechanisms to proactively identify and address issues like model drift, performance degradation, or infrastructure failures.

• API and Service Development: Develop clean, well-documented, and efficient APIs (e.g., REST, gRPC) using frameworks like FastAPI or Flask to serve model predictions to downstream applications. Ensure the security, authentication, and scalability of the AI service endpoints.
• Cross-Functional Collaboration: Work closely with AI scientists, product managers, software engineers, and UX designers to align AI solutions with business needs.: Work closely with the AI Scientist to understand model architecture and dependencies, providing feedback to improve its "deployability." Partner with the lead Generative AI Scientist to ensure that the production environment can meet the performance and cost requirements of their proposed solutions.

• Ensuring Responsible & Secure AI: Implement AI solutions in line with ethical and security guidelines . The AI Engineer is expected to incorporate principles of Responsible AI – ensuring models are fair, transparent, and privacy-compliant. They must guard against risks (bias, data leakage, prompt injection attacks on agents, etc.) and follow security best practices for AI deployments. For instance, when deploying an agentic AI system that can take actions, the engineer will put guardrails in place to prevent unintended behaviors and to log decisions for auditability.

• Continuous Learning & Improvement: Stay up-to-date with the latest AI research and emerging tools, and continuously improve AI models in production. AI Engineers track new developments (new model architectures, improved frameworks like LangChain updates, etc.) and assess how these can enhance existing systems. They also gather user feedback and model performance data to iteratively refine the solutions (e.g. fine-tuning an LLM for better accuracy or reducing latency by optimizing code).

Skills:

• Strong Programming & Scripting: Expert-level Python; proficiency in shell scripting (Bash).
• Cloud Expertise: Deep, hands-on experience with either GCP (GKE, Cloud Run, Vertex AI, Cloud Build) or Azure (AKS, Azure ML, Azure Functions, Azure DevOps) it AWS (EKS/ECS/SageMaker/Lambda/AWS Developer Tools). Experience in more than one is a major advantage.
• MLOps & DevOps Tooling : Containerization & Orchestration : Docker (Expert), Kubernetes (Strongly Required). CI/CD: Experience building pipelines with tools like GitHub Actions, Jenkins, Azure DevOps, or CircleCI. Infrastructure as Code Familiarity with Terraform or similar tools is highly desirable. Monitoring: Experience with tools like Prometheus, Grafana, or cloud-native monitoring services (e.g., Google Cloud Monitoring, Azure Monitor).
• Model Serving & Optimization: Practical experience with modern model serving frameworks (e.g., Triton, TorchServe, KServe, vLLM). Knowledge of model optimization techniques (quantization, pruning) is a significant plus. API Development : Proficiency in building APIs with frameworks like FastAPI, Flask, or similar.

Behavioral Competencies:

• Systems Thinker: Ability to see the entire system and understand how different components interact, from the model code to the underlying infrastructure.
• Automation-First Mindset: A strong drive to automate repetitive tasks and build resilient, self-healing systems.
• Performance and Efficiency Driven : A relentless focus on optimizing performance, latency, and cost.
• Reliability and Ownership: A deep sense of responsibility for the stability and performance of systems in production.

Experience & Education:

• Bachelor’s or Master’s Degree in Computer Science, Software Engineering, or a related technical field.
• Minimum 3-5+ years of hands-on experience in a DevOps, MLOps, or Software Engineering role with a focus on deploying and managing machine learning systems in production.

Position Requirements:

• Ability to travel up to 10% domestically and internationally.

What We Offer

• Solve the most challenging engineering problems at the intersection of AI and cloud infrastructure.
• Be the expert who makes cutting-edge AI research a practical reality at enterprise scale.
• Work in a collaborative, high-impact team where your engineering skills are essential to the success of our AI strategy.
• Gain deep expertise in the rapidly growing field of MLOps for Generative AI.

Your Responsibilities

• Research, develop, benchmark, test and document state of the art algorithms for autonomous driving.
• Research new vision and other sensor technology, ideas, and approaches.
• Support Intellectual Property activities and generate Invention Disclosure Memos to facilitate patent applications.
• Contribute to design reviews across algorithm teams.
• Consider next generation algorithms and new business opportunities.
• Develop algorithms with a view to implementing on embedded platforms.
• Represent Valeo anSWer at various Industry Working Groups and at Conferences.
• Support the design and implementation of in-company training sessions in your area of expertise.
• Strive to improve competencies in the field of automotive sensors based ADAS, DL frameworks & techniques.
• Identify inefficiencies and suggest/implement improvements within the current processes used in algorithm development

Minimum Qualifications

• Bachelors or Masters in Computer Science, Artificial Intelligence, Data Science, or related field
• 2-6 years relevant experience in AI / DL development
• Strong knowledge of deep learning concepts and computer vision
• Experience with object detection models such as YOLO and semantic segmentation
• Expertise in python programming and strong software engineering practices
• Proficiency with version control tools such as git, bash scripting, and automation
• Excellent analysis & trouble-shooting skills using a structured documented approach
• Excellent communication skills, both written and verbal
• Confident, collaborative, self-motivated, determined & energetic

Highly Desirable Qualifications

• Specialization in one or more of these areas preferred: Transformers, Multi-task learning, Optimization, and Multi-modal models.
• Experience designing complex systems with multiple algorithms or modules
• Academic publications in top-tier conferences such as CVPR, NeurIPS, ICCV, and ECCV.
• Experience with commercially deploying algorithms in robotics, LIDAR, camera systems, or sensor fusion.
• Experience developing algorithms for autonomous and/or real-time systems.
• Deep knowledge of camera systems.
• Automotive industry experience.