Top Machine Learning Jobs in Chennai

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Leads AI innovation across HR SaaS products by owning the AI software development lifecycle, designing agentic and RAG systems, building prototypes, defining production specifications, guiding engineers, and improving in-house AI platforms. Responsibilities include prompt and context engineering, evaluation, monitoring, model optimization, orchestration, governance, and mentoring teams while scaling reliable AI features.

Ensures the quality, reliability, and performance of AI products, model outputs, datasets, and supporting data pipelines. Responsibilities include designing AI/ML test strategies, evaluating prompts and LLM responses, detecting hallucinations and bias, validating ETL/ELT processes and dataset integrity, defining model quality metrics, automating API and data validation tests, integrating tests into CI/CD, and collaborating with engineering, data science, product, and QA teams.

Lead product strategy and roadmap for AI-powered Digital Assistant across quote-to-cash workflows. Translate business needs into product requirements, collaborate with data scientists, ML engineers, designers and software teams, prioritize features, monitor metrics, ensure ethical AI use, drive execution from ideation to launch, and communicate with stakeholders through demos and training to improve support efficiency and customer satisfaction.

Lead product strategy and roadmap for AI-powered Digital Assistant across quote-to-cash workflows. Translate business needs into requirements, collaborate with ML and engineering teams, prioritize features, monitor performance, and ensure ethical AI. Drive cross-functional alignment, build business cases, and deliver demos to stakeholders.

Lead strategy, vision, development, and growth for an agentic AI platform that automates complex enterprise tasks. Responsibilities include customer and market discovery, competitor analysis, feature prioritization, backlog and roadmap management, agile delivery, and collaboration with engineering, AI/ML, design, sales, marketing, and solutions teams. The role also supports product demos, enablement, client presentations, adoption, and revenue growth.

Architect and oversee scalable, real-time software platforms for engineering simulations and operational mobility tools. Define system architecture, module boundaries, APIs, data objects, execution models, and integration traceability. Lead platforms from prototype through production, write low-level requirements, establish development and testing standards, and support ML and GenAI implementations. Analyze timing, CPU, memory, and distributed-system performance while collaborating with technical teams throughout proof-of-concept, deployment, and optimization phases.

Develop and deploy statistical and machine learning models for fraud detection, credit risk scoring, portfolio management, pricing, and related financial applications. Collaborate with data engineering, DevOps, MLOps, and business stakeholders to capture data, understand domain requirements, build production ML pipelines, and optimize model training throughput and scoring response times. Support model development, testing, monitoring, and deployment at scale.

The Staff Product Manager will lead product backlog for a Scrum team, collaborating closely with engineering and AI teams while driving customer outcomes and improvements through analytics and integration solutions.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Develop and apply machine learning innovations, primarily for NLP, to large-scale business problems. Conduct feasibility studies, analyze data, evaluate real-world applicability, and build scalable, maintainable, reproducible ML solutions. Collaborate on deployment and integration, communicate findings to technical and nontechnical stakeholders, understand business requirements, and contribute to team development and collaboration.

Lead algorithm design and development for wafer inspection products, delivering image processing, computer vision, and machine learning solutions. Define performance requirements, mentor engineers, support system integration, validate and deploy algorithms, and ensure software quality and reliability.

Leads a global portfolio of process and analytics initiatives across pharmaceutical operations. Owns opportunity prioritization, value realization, project delivery, governance, risk management, stakeholder alignment, change adoption, and talent coaching. Uses process mining, automation, AI, and advanced analytics across SAP, Veeva, and Salesforce to improve efficiency, reduce risk, and deliver measurable financial and operational outcomes.

Manage strategic enterprise customer relationships, serving as the primary contact for technical support, training, issue resolution, and executive engagement. Partner with sales on account health, growth, and presales activities while advocating for customer needs internally. Apply data analytics, visualization, AI tools, and automation to improve reporting, insights, troubleshooting, and customer value. Support connectivity-focused products and regularly meet clients and attend company functions.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Develop and optimize machine learning, deep learning, and generative AI models for business and customer-experience applications. Build LLM and RAG solutions, prepare datasets, perform feature engineering, evaluate models, and conduct experimentation. Collaborate with Marketing, Sales, Product, and Engineering teams to translate business needs into measurable AI solutions. The role also involves dashboarding, executive reporting, cloud platforms, and MLOps practices.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Develop AI and machine learning applications using cloud AI services and generative AI models. Implement deep learning, neural networks, chatbots, image processing, statistical analysis, and machine learning algorithms in production-ready systems. Independently contribute as a subject matter expert, collaborate with teams, solve technical problems, and remain current with AI and ML technologies.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

Leads the company’s machine learning research and development strategy, focusing on multimodal generative models for video, audio, and language. Designs architectures and large-scale training pipelines, builds and mentors a high-performing ML team, and drives deployment of research innovations into production. Collaborates with product, engineering, and creative teams, establishes ML standards, evaluates emerging technologies, and represents the company through research publications and conferences.

Designs, develops, and deploys production-ready AI/ML applications using Python and cloud AI services. Implements generative AI, deep learning, neural networks, chatbots, and image-processing solutions. Leads teams as an SME, makes cross-team technical decisions, mentors junior engineers, and coordinates cross-functional efforts to ensure scalable, reliable AI systems in cloud or on-prem pipelines.

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.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Design, build, and deploy production-ready AI/ML applications using generative AI and cloud AI services. Lead solution architecture, integrate GenAI models, develop deep learning and image-processing components, maintain cloud and on-prem pipelines, and mentor teams while influencing cross-team technical decisions.