Top Machine Learning Jobs in Chennai

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

Conduct security research involving malware, threats, vulnerabilities, adversary tracking, threat intelligence, detection, remediation, and reverse engineering. Analyze threat trends, process intelligence reports, identify security defects, contribute code and automation, support incident response, and use research results to improve security products and services. The role focuses on cloud-scale threat detection using machine learning and behavioral profiling.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Build and deploy engineering-grade digital models for Japanese utility clients by integrating GIS, LiDAR, and other data sources. Configure platform projects, create dashboards, resolve model inaccuracies, support technical delivery and pre-sales, and collaborate with product and engineering teams on platform improvements and Japanese localization. The role requires customer-facing communication in Japanese, global collaboration in English, mentoring, and occasional travel to customers and international offices.

Leads the design and development of generative and agentic AI applications using LLMs, NLP, deep learning, retrieval, RAG, and orchestration frameworks. Builds agent architectures, evaluation frameworks, data-processing pipelines, guardrails, and responsible AI controls. Optimizes models through prompting, reranking, embeddings, model selection, and fine-tuning. Translates business needs into measurable AI solutions, stays current with emerging technologies, leads technical discussions, and mentors team members.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Leads the architecture, development, and production delivery of AI/ML capabilities in enterprise healthcare applications. Builds ML and generative AI solutions, RAG systems, model-serving APIs, MLOps workflows, distributed cloud services, and CI/CD pipelines. Ensures responsible AI, security, privacy, reliability, monitoring, and operational readiness. Influences architecture across teams, mentors engineers, documents technical decisions, and delivers reusable production components.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

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.

Leads mainframe modernization and migration initiatives for large global clients. Develops transformation and re-platforming solutions, conducts discovery workshops, presents technical and business cases, advises executives, and partners with sales, consulting, operations, and product teams. Requires extensive mainframe expertise across legacy technologies, migration architecture, data modernization, cloud and hybrid environments, and enterprise hosting. The role also supports AI-driven modernization, technical documentation, project lifecycle management, and client relationship development.

Design and implement production-ready AI and machine learning applications using Google Cloud Platform and cloud AI services. Integrate generative AI models into scalable cloud or on-premises pipelines, with potential work in deep learning, neural networks, chatbots, and image processing. Lead technical design, coordinate cross-functional integration, make team decisions, provide expertise across teams, and mentor engineers. Optimize AI workloads for performance and cost while managing model deployment and lifecycle processes.

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.

Plans and coordinates cross-functional AI/ML, software, and data projects from concept through production using Agile and NPI frameworks. Manages roadmaps, backlogs, risks, dependencies, stakeholder communication, project reporting, KPIs, business impact, and savings validation. Facilitates Scrum ceremonies, supports data and model delivery, tracks financial status, and drives continuous improvement across innovation projects.

Design, develop, deploy, and integrate AI, machine learning, and generative AI solutions for industrial and business applications. Own the machine learning lifecycle, define architectures, assess data readiness, monitor production models, and ensure reliability and maintainability. Evaluate emerging AI technologies, manage technical delivery from partners and contractors, and collaborate with cross-functional stakeholders to identify use cases, support adoption, and deliver measurable operational improvements.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business applications. Owns the machine learning lifecycle, solution architecture, data strategy, model monitoring, and production integration. Evaluates emerging technologies, manages technical delivery from partners and contractors, and collaborates with cross-functional teams to deliver scalable, reliable, and maintainable AI solutions.

Designs, develops, deploys, and integrates AI, machine learning, generative AI, and analytics solutions for industrial and business challenges. Owns the machine learning lifecycle, solution architecture, data strategy, production integration, monitoring, and technical evaluation of internal and external partners. Collaborates with cross-functional stakeholders, supports adoption, evaluates emerging AI technologies, and develops solutions such as predictive maintenance, anomaly detection, forecasting, document analysis, and knowledge tools.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business challenges. Responsibilities include selecting modeling approaches, managing the ML lifecycle, defining solution architectures, analyzing operational data, supporting production deployment and monitoring, managing technical partners, and ensuring data governance, reliability, and maintainability. The role also evaluates emerging technologies, collaborates across functions, and supports user adoption and continuous innovation.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Conduct applied and fundamental research in reinforcement learning, agentic AI, multimodal reasoning, and foundation-model adaptation for semiconductor inspection and metrology. Develop intelligent systems that plan, use specialized tools, learn from feedback, and make sequential decisions. Design rigorous experiments, evaluate reliability and failure recovery, build reproducible prototypes, and translate research into scalable industrial solutions. Collaborate with multidisciplinary teams and manage engineers working in AI.

Leads AI engineering teams and builds scalable enterprise AI platforms supporting generative and agentic AI applications. Oversees platform architecture, deployment, reliability, governance, model lifecycle management, KPIs, and engineering standards. Develops global teams, mentors technical talent, manages strategic initiatives, and advises stakeholders on AI solutions. Drives responsible AI practices, innovation, partnerships, and adoption of emerging technologies across the enterprise.

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.

Design, develop, and maintain scalable software solutions integrating AI and machine learning models into commercial products and internal processes. Collaborate with data scientists, product managers, and cross-functional stakeholders to translate business needs into technical solutions. Write high-quality code, support software architecture and design reviews, improve development processes, and follow Agile and NPI/OTR methodologies while contributing to product introductions, cost improvements, and problem resolution.