Top Machine Learning Jobs in Chennai

Designs, develops, and maintains scalable software solutions integrating AI and machine learning models into commercial products and internal processes. Collaborates with data scientists, product managers, and cross-functional stakeholders to translate business needs into technical solutions. Responsibilities include software architecture, coding, testing, documentation, technical reviews, process improvement, product introduction, and adherence to Agile and NPI/OTR methodologies.

The Product Manager will define and drive AI-powered statistics for life sciences, collaborate across teams, engage with customers, and support market differentiation strategies.

Associate Product Manager responsible for understanding customer needs, defining product opportunities, creating requirements and roadmaps, partnering with Engineering and Design, running experiments, analyzing metrics, and improving lending product outcomes. The role collaborates with Risk, Operations, Data Science, Compliance, and external partners while independently owning product initiatives and contributing to long-term product strategy.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Designs enterprise-scale AI, Generative AI, and Agentic AI architectures and platforms. Leads implementation of LLM, RAG, conversational AI, knowledge assistant, and multi-agent solutions; defines AI strategy, governance, observability, scalability, security, and responsible AI practices. Collaborates with stakeholders, engineers, data scientists, and delivery teams to translate business needs into AI solutions. Provides architecture reviews, customer engagement, technical leadership, and mentorship to AI engineering teams.

Supports the design, development, testing, deployment, and maintenance of cloud-based software in a microservices environment. Works with APIs, telemetry, structured and unstructured data, distributed systems, and ML/AI algorithms. Assists with automation, code reviews, refactoring, documentation, CI/CD, testing, and software integration while collaborating with product teams and stakeholders.

Lead the design, development, and deployment of AI/ML systems for pricing, promotion optimization, demand forecasting, and price elasticity modeling. Build time-series, causal inference, reinforcement learning, recommendation, and optimization models across B2C and B2B channels. Architect production ML pipelines, data workflows, APIs, monitoring, retraining, and drift detection using cloud and data warehouse platforms. Collaborate with engineering, product, marketing, and analytics teams to improve profitability, revenue, and pricing decisions.

Leads delivery of enterprise conversational AI platforms, including chatbots and voice bots. Oversees Agile execution, technical architecture, cloud-native engineering, CI/CD, Terraform, Responsible AI, and AI adoption. Partners with product, security, architecture, and business teams while managing risks and executive communication. Mentors engineers and technical leads, supports hiring and performance management, and ensures scalable, secure, compliant, and operationally excellent solutions.

The IT GRC Risk and Automation Analyst manages enterprise GRC platforms, integrated risk tools, automated reporting, API integrations, evidence-collection workflows, and continuous compliance monitoring. The role conducts third-party security assessments and translates technical risks into business impacts. It requires expertise in security, privacy, AI governance, and risk frameworks, including NIST CSF, ISO 27001, SOC 2, GDPR, NIST AI RMF, ISO 42001, and the EU AI Act.

Designs and maintains secure CI/CD pipelines for conventional software and ML models in regulated aerospace environments. Responsibilities include configuration baselining, release management, software quality assurance, lifecycle audits, traceability, corrective-action investigations, change-control governance, automated testing, and compliance with DO-178C, DO-254, and ARP4754B. The role also supports containerized deployments, safety-critical releases, and testing on physical target hardware.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Design, train, fine-tune, deploy, and optimize production AI systems including LLMs, RAG pipelines, agentic workflows, and machine learning solutions. Build APIs and inference pipelines, evaluate quality and performance, monitor production systems, and translate research into scalable applications. Collaborate with engineering, product, leadership, and global research teams while working a required overnight India schedule aligned with U.S. hours.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Develop ADAS AI/ML solutions focused on computer vision and radar algorithms. Responsibilities include image processing, mono and stereo camera systems, LiDAR point-cloud processing, coordinate transformations, camera calibration, 3D reconstruction, depth imaging, SLAM, and production-grade deep learning models. The role also requires understanding radar, imaging and optics systems, neural networks, machine learning techniques, Kalman and particle filters, and clustering methods.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Develop and deploy scalable AI and machine learning solutions using Generative AI, LLMs, RAG, NLP, and Computer Vision. Manage model lifecycles with Docker, Kubernetes, and MLOps practices. Optimize vector databases, prompt engineering, and model performance while conducting SQL-based data analysis and statistical modeling. Integrate AI capabilities into business workflows and maintain reliable, scalable deployment pipelines.

Design, develop, and maintain compiler front-end, middle-end, and back-end components. Implement language features, code generation, optimization algorithms, and customer-specific compiler solutions. Collaborate with customers and cross-functional teams to translate requirements into technical specifications, stay current with compiler technologies, and mentor junior engineers.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Designs and delivers production AI/ML systems for enterprise clients, including generative AI, agentic AI, classical ML, data pipelines, and MLOps on cloud platforms. Leads engineers, owns architecture and production operations, runs client discovery, presents to executives, manages scope and risk, and ensures successful handoff. The role is hands-on, client-facing, and requires collaboration with U.S.-based teams on a shifted India schedule.

Leads an integrated data, AI, analytics, and product engineering organization. Owns strategy, roadmaps, architecture, technical standards, production service levels, AI governance, client relationships, budgets, hiring, and organizational growth. Directs delivery of data platforms, dashboards, machine learning models, generative AI solutions, and full-stack applications while ensuring measurable business value, operational stability, responsible AI practices, and strong stakeholder governance.

Architects and deploys production-grade agentic AI and multi-agent systems using LLMs, RAG, vector databases, prompt engineering, and NLP. Designs autonomous workflows, tool calling, memory, routing, evaluation, observability, and guardrails. Builds backend APIs and microservices while implementing testing for model drift, prompt regression, and agent reliability. Requires advanced Python, hands-on production Generative AI experience, and expertise with agent orchestration frameworks and vector search.

Evaluate, benchmark, deploy, and optimize AI models for on-device execution on Android hardware. Responsibilities include integrating models into applications and system services, reducing latency, memory use, and power consumption, applying quantization and compression, leveraging CPU, GPU, and NPU acceleration, and building hybrid local/cloud inference routing with agent systems.