Machine Learning jobs in India

As a Data Science Manager in Bengaluru, you'll lead a team to develop data-driven solutions for the fintech industry. Your role will involve turning data into actionable insights to support business goals.

Lead the design and deployment of AI, Machine Learning, and Generative AI solutions while driving enterprise AI strategy and cloud-based innovation. Partner with business and technology teams to deliver scalable, data-driven products with measurable business impact.

The incumbent will lead the adoption of AI/ML, Generative AI, and Agentic AI to create measurable business value-enhancing customer experience, unlocking cross-sell and up-sell opportunities, improving productivity, and strengthening fraud and risk management.

As an FDE in the technology industry, you'll work on creating, improving, and maintaining software solutions. You'll ensure systems run smoothly and meet user needs in a busy technology environment.

This role will design, build and scale an enterprise-grade AI agent that reasons over workforce data to deliver trusted operational and productivity insights. The successful candidate will own the full AI stack, from agent orchestration and RAG architecture to evaluation frameworks, observability and production reliability.

This role combines advanced data science, machine learning, and software engineering to develop innovative AI-driven solutions. The successful candidate will help deliver predictive models, Generative AI applications, and production-grade software while collaborating across product and engineering teams.

This is a hands-on AI Engineering Lead role responsible for building and leading a team developing agentic AI platforms, intelligence engines, and AI-powered productivity governance products. The position combines technical leadership, architecture ownership, people management, and the opportunity to define how AI-native software is built across the organisation.

As an SSE Agentic in the pharmaceutical industry, based in Hyderabad, you'll focus on-

Design and scale production-grade AI agent platforms.

Lead architecture for LLM, RAG and autonomous workflows.

• Lead enterprise analytics, AI/ML and automation delivery teams.
• Drive business transformation through data and leadership.

• Own the AI vision, roadmap, and enterprise AI innovation.
• Lead AI research, data science, and customer solutions teams.

The Lead Research Engineer, Applied ML role is a senior technical leadership position responsible for owning end-to-end machine learning and LLM workstreams focused on oncology clinical data and healthcare AI applications. The role combines hands-on ML development, architectural leadership, mentoring, and cross-functional collaboration to deliver production-grade AI systems at scale.

Lead the data science team to develop innovative analytics solutions for private banking within the financial services sector. You'll drive data-driven strategies and insights that support business decision-making

We are looking for a Lead ML Research Scientist to drive the research vision, model training strategy, and innovation roadmap for domain-specific AI systems focused on embedded and hardware-centric engineering environments.

The role combines hands-on LLM research, reinforcement learning, synthetic data generation, and research leadership to build AI models that solve real-world engineering challenges.

Looking for a Data Science Tech Lead to lead analytics-driven operational decision-making at the intersection of data science, business analytics, and operations management.

This role combines strategic leadership, stakeholder management, team development, and analytical problem-solving to drive measurable business outcomes.

Lead enterprise data, analytics and AI strategy.

Drive ML, GenAI and Agentic AI at scale.

Lead AI/ML team building enterprise solutions and scalable systems

Drive end-to-end AI delivery from development to deployment

This role as a Senior AI Engineer in the Financial Services industry focuses on developing and implementing advanced AI models to enhance business analytics and decision-making. Based in Bangalore, the position requires expertise in AI technologies to deliver innovative solutions for financial consultancy challenges.

This role focuses on building production‑grade applied AI systems, including LLM applications, RAG pipelines, and AI agents. You will work across data, reasoning, and backend systems to deliver personalized learning experiences at scale.

The role involves leading the design, development, and deployment of production‑grade AI/ML solutions while owning end‑to‑end delivery across multiple projects and mentoring a small team of engineers. It combines deep hands‑on technical work in areas like deep learning, NLP, computer vision, and MLOps with strong leadership and cross‑functional collaboration to deliver real‑world impact.

The Supervisor Software Dev role in the professional services industry requires a skilled individual to oversee software development projects and ensure the delivery of high-quality solutions. This permanent position in Dallas is ideal for someone with a strong understanding of analytics and consultancy-based project management.

The position is a senior leadership role within the Client's data science organization, responsible for driving strategic analytical solutions, nurturing a high‑performing team, and shaping the company's growth. It requires deep client engagement, problem‑solving expertise, and the ability to convert complex business challenges across multiple industries into scalable data‑driven solutions.

The Principal Data Scientist will design, build, and optimize advanced NLP, computer vision, and LLM‑powered systems that automate permitting and regulatory workflows for a leading GovTech platform. The role combines deep technical ownership with strategic leadership, driving AI innovation that directly shapes product direction and government efficiency at scale.

The Senior Data Scientist will own end‑to‑end development of machine learning models that power their cutting‑edge, human‑like voice AI used across major BFSI enterprises. The role involves building scalable NLP and speech intelligence systems, optimizing real-time conversational performance, and driving innovation in a fast‑paced, high‑ownership startup environment.

Build predictive analytics and machine learning solutions to improve customer growth, retention, and lifetime value.

Partner with business leaders to deliver actionable insights and drive data-led decision making.

Drive customer analytics through advanced machine learning.

Build predictive models to improve growth and retention

The role of Manager ML Engineering involves developing and deploying machine learning solutions to address complex business challenges within the FMCG sector, particularly in the Beverages & Alcohol segment. The position is based in Bengaluru Urban and requires expertise in analytics and machine learning technologies to drive data-driven decision-making.

The Director - Data Science is a senior leadership role responsible for leading the design and deployment of advanced AI solutions, including Agentic AI, Large Language Models (LLMs), Machine Learning, and intelligent automation capabilities, while ensuring they are scalable, secure, and production ready. The role combines deep technical expertise with strategic product leadership to build innovative, enterprise-grade AI products

As Technical Lead - Data Science & Generative AI, you drive the design, development and deployment of machine learning, deep learning and generative AI solutions across Incedo's platforms and client programmes. You work alongside data scientists, data engineers and business stakeholders to ship scalable, productionready AI/ML applications, shape the technical roadmap for AI initiatives, and mentor the team that builds it

The AI Tech Lead will deliver intelligent systems from architecture to production at scale. They should be skilled in designing robust AI platforms, data pipelines, and MLOps frameworks, with strong focus on scalability, reliability, and performance optimization. They should also have experience in applying advanced techniques across machine learning, deep learning, NLP, and GenAI (including RAG and LLM-based systems), while embedding responsible AI practices.