Top Machine Learning Jobs in Chennai

Evaluate scientific manuscripts in computer science, engineering, data science, machine learning, and related fields; coordinate peer review; provide author feedback; engage researchers through outreach and conferences; recruit high-quality submissions; strengthen journal visibility; and contribute to editorial strategy, journal development, and broader science communication initiatives.

Manage and evaluate scientific manuscript submissions in computer science, electrical engineering, data science, machine learning, and related fields. Coordinate peer review, provide author support, recruit research papers through scientific outreach, attend conferences and academic visits, and help strengthen journal visibility. The editor will also contribute to journal strategy, science communication initiatives, and collaborative editorial planning.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

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.

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.

Develop production-ready AR/VR models and digital content for vehicle aftersales applications. Build systems using Unity and Unreal Engine, interpret wiring diagrams, integrate sensor data, and apply sensor fusion techniques. Develop computer vision and machine learning models using Python and relevant frameworks, with advanced knowledge of supervised, unsupervised, and reinforcement learning. Produce high-quality production code in C#, C++, and Python for AR/VR vehicle servicing solutions.

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.

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.

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.

Owns end-to-end IT services presales, including opportunity qualification, solution development, bid management, RFP responses, proposals, presentations, pricing, and deal closure. Partners with sales, delivery, architects, product managers, and subject matter experts to create feasible, differentiated solutions. Conducts market and competitive analysis, supports go-to-market strategies, and develops value propositions across product engineering, AI, cloud, and data services.

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.

Data Science Interns use machine learning, statistics, experiments, algorithms, and data exploration to solve business and product problems. They formulate analytical approaches, interpret and validate results, collaborate with stakeholders, produce actionable insights, and help improve Microsoft products and customer experiences through peer review and iterative analysis.

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.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

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.

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.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.