Top Machine Learning Jobs in Chennai

Analyze structured and unstructured data, build and maintain Tableau and Adobe Analytics dashboards, perform ad-hoc KPI reporting, validate data quality, generate actionable insights through storytelling, support AI/ML initiatives (NLP, chat transcript and conversational analytics), and automate recurring reports.

Lead technical product lifecycle from strategy to end-of-life with an AI focus. Define product vision, prioritize roadmaps, decompose features into user stories, partner with engineering and stakeholders, leverage AI/ML insights, support CI/CD and Agile delivery, monitor product performance, and drive adoption and governance of AI-enabled solutions.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Lead research and engineering of cutting-edge AI models, fine-tune LLMs, design agentic systems, prototype novel algorithms, optimize training pipelines, benchmark and deploy scalable RAG-powered solutions, ensure ethical AI practices, and mentor junior researchers while integrating AI into production products.

Lead research and development in AI/ML, design scalable pipelines, implement models, and collaborate with cross-functional teams for optimal solutions.

Design, prototype, and benchmark advanced AI/ML algorithms and agentic systems; optimize large-scale training pipelines; integrate generative AI, RAG, and Azure OpenAI solutions into production; collaborate with cross-functional teams; ensure ethical, interpretable, and robust model deployment.

Lead execution of post-sales product initiatives: roadmap planning, discovery, PRDs, AI/workflow automation, engineering collaboration, KPI dashboards, stakeholder alignment, launch enablement, and platform/backlog management.

Lead a team delivering high-quality retailer data for US markets: validate, troubleshoot, document processes, drive automation (R/Python/Excel Macros), coordinate with stakeholders, monitor KPIs, and manage projects to improve data quality and on-time delivery.

Analyze large datasets, build and evaluate machine learning models, prepare and transform data, and create Power BI dashboards. Apply statistical methods and A/B testing, use cloud platforms (AWS, Snowflake), leverage Generative AI/LLMs, ensure data governance and responsible AI, and present insights while collaborating in an Agile team.

Designs and implements production-ready AI/ML solutions using cloud AI services and GenAI. Leads and mentors teams, collaborates cross-functionally, develops scalable deep learning models (TensorFlow/PyTorch), performs data preprocessing and feature engineering, and evaluates new technologies for system performance improvements.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Design and implement machine learning and NLP solutions, build prototypes and demonstrations, work with LLMs and deep learning frameworks, and leverage cloud services to productionize AI models.

Design, build, and deploy conversational AI/chatbot solutions on the Kore.AI platform. Create intents, entities, dialog flows, integrate backend systems and REST APIs, and develop responsive user interfaces with ReactJS while collaborating with UI/UX designers.

Design, build, and automate secure AWS infrastructure and CI/CD pipelines. Ensure availability, performance, scalability, and compliance (PCI‑DSS, HIPAA, SOX, GDPR, CCPA). Manage releases/configuration, container and API security, serverless solutions, security automation/ML, monitor systems, and publish CICD best practices.

Lead the AI Capability Centre, driving strategic development and operational excellence in AI solutions while managing a team of AI experts.

Design, build, and configure ML-driven applications to meet business requirements. Act as an SME, lead and manage teams, collaborate across teams, make technical decisions, ensure timely delivery, and facilitate knowledge sharing to produce robust, user-friendly solutions.

Label and validate datasets (documents, images, video, audio) for document fraud, deepfake, PAD, and face recognition. Create bounding boxes, landmarks, time-based frame labels, and flag tampering. Document edge cases, review peer annotations, ensure quality standards, and collaborate with ML engineers to improve datasets and tools.

Lead end-to-end AI solutions for clients, manage project delivery and people, partner with sales for business development, collaborate with engineering/MLOps/DevOps, ensure data quality, model training, tuning, deployment, monitoring, and provide executive updates on status and risks.

The role involves qualifying products, scripting automated tests, interacting with cross-functional teams, and assessing automation feasibility while using AI tools. Travel to customer sites is also required.

Lead and grow a software engineering team to design, architect, and deliver robust, scalable wafer inspection software. Drive adoption of next-generation architectures and modern technologies, maintain legacy systems, collaborate across functions and geographies, and meet product timelines with high-quality, performance-sensitive code and ML-informed improvements.

Design and develop data solutions using Power BI and Snowflake. Perform data mining, feature engineering, time-series and IoT analytics, build predictive models and automated anomaly detection, maintain data pipelines and quality, and collaborate in an agile team to deliver analytics integrated into product systems.

Manage cross-team delivery in an AI-native engineering organization, focusing on risk management, dependency resolution, and technical communication.

Lead the ethical use and governance of AI, ensuring compliance with legal standards, managing AI risks, and fostering collaboration across teams.

Design, prototype, and productionize computer vision and machine learning algorithms for semiconductor wafer inspection. Develop and optimize image processing for defect detection and classification, ensure real-time scalability and performance, and collaborate with cross-functional teams to integrate solutions across Windows and Linux environments.

Design, develop, and deploy cloud-native chatbots and voice bots using Dialogflow CX and Google CCAI. Build LLM-powered playbooks, integrate backend APIs and databases, apply NLP/ML techniques (TTS/STT/SSML), troubleshoot distributed systems, and collaborate with cross-functional teams to deliver scalable conversational AI solutions.