Top Machine Learning Jobs in India

Develop and maintain Python applications, frameworks, APIs, automation scripts, and integrations. Analyze data using Pandas, NumPy, and Matplotlib; extract text with OCR; identify tool issues and implement AI-oriented solutions. Manage IT systems, assets, and infrastructure while writing tests, optimizing performance, documenting processes, and supporting cross-departmental integration.

Develop and maintain high-performance 3D modeling software using advanced geometry processing, rendering, simulation, and visualization techniques. Integrate 3D assets, graphics APIs, game engines, and emerging VR, AR, and AI technologies. Build testing frameworks, troubleshoot complex issues, document software, conduct code reviews, provide technical support, and mentor junior developers while collaborating with designers, artists, engineers, and QA teams.

Develop and test C++ software for high-performance embedded Linux systems and automotive infotainment head units. Responsibilities include optimizing applications for x86, ARM, NEON, and CUDA architectures; designing software frameworks, modules, requirements, and interfaces; managing integration, builds, and releases; documenting designs and tests; and flashing and testing applications on embedded hardware. The role also involves supplier coordination, requirements clarification, and collaboration within Agile teams.

Develop end-to-end data science and AI/ML solutions involving statistical modeling, machine learning, generative AI, LLMs, NLP, and unstructured data analytics. Build data models and algorithms, solve ambiguous business problems, collaborate with project managers and multiple teams, and deliver actionable insights using cloud platforms such as AWS, Azure, or GCP. Present findings and communicate effectively with technical and business stakeholders.

Develop and deploy generative AI applications, integrate LLM APIs, perform text analysis using traditional machine learning and GenAI, and manage the machine learning lifecycle from problem definition through deployment and monitoring. Collaborate with cross-functional teams, translate business needs into technical solutions, evaluate new GenAI techniques, and document technical concepts.

Leads end-to-end data science and AI projects, including modeling, testing, deployment, and value realization. Manages multidisciplinary teams, coordinates with Product, IT, Climate, Risk, GIS, and business stakeholders, and applies peril modeling, climate risk, insurance, and geospatial expertise. Maintains the DSAI operating model, establishes development best practices, and oversees team capability development, mentoring, and performance management.

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.

Deliver and create online and offline data science training, mentor aspiring data professionals, develop accessible training content, solve business problems using machine learning, and promote data science skills within the learning community.

Research emerging AI developments across videos, blogs, papers, and other resources. Create accessible educational materials such as slide decks, notes, scripts, summaries, documentation, and training resources. Simplify complex concepts including RAG, LLMs, LLaMA models, and agentic workflows. Collaborate with academic and content teams to ensure accuracy, clarity, consistency, and learner engagement.

Create and deliver online and offline data science training, mentor aspiring data professionals, help students solve data problems, curate educational content, learn and evangelize new skills, and lead strategies that make data science accessible. The role requires strong machine learning, visualization, storytelling, business problem-solving, communication, and training abilities.

Assist with designing, developing, testing, and deploying machine learning models. Analyze and preprocess datasets, collaborate with data scientists, engineers, and product teams, document experiments and workflows, and conduct literature reviews to stay current with AI and machine learning research.

Collects, cleans, and analyzes large datasets; performs exploratory analysis and statistical modeling; develops predictive and classification machine learning models; writes SQL queries; creates Power BI dashboards and Excel reports; and presents findings to stakeholders while collaborating on business data solutions.

Senior embedded software engineer responsible for sustaining engineering: translate system requirements into software specs, design and implement embedded control applications, develop and run tests, produce documentation (design, test plans, FMEA), collaborate with hardware and cross-functional teams, and drive technical problem-solving for medical device products.

The Staff Software Engineer will develop AI/ML models and solutions, collaborate on design and implementation, and support R&D efforts in the healthcare industry.

Lead design and implementation of cloud-native, AI-driven security operations automation. Build scalable backend services, detection pipelines, and automated incident response using Python/Go, Kubernetes, AWS, CI/CD, Terraform, and AI/ML. Provide architectural leadership, mentor engineers, and operationalize LLMs and agentic frameworks for SOC workflows.

The Machine Learning Operations Engineer is responsible for designing and maintaining ML pipelines, ensuring efficient model deployment, monitoring model performance, and optimizing infrastructure for ML workflows.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.

As a Junior-Midlevel Developer, you will develop, maintain, and enhance an AI-powered education platform, collaborating with teams to build software and optimize performance.

Lead development and operation of Nike's multi-cloud access, entitlement, governance, and cost-optimization platform. Drive architecture, roadmap, squad leadership, SLO-driven operations, AI-assisted automation, and cross-region co-ownership while mentoring engineers and ensuring reliable, secure management plane services.

The ROR Developer will focus on software engineering practices, design, data structures, algorithms, and deliver projects with responsibility, while keeping pace with industry trends.

The Data Scientist will leverage critical thinking and data analysis skills to develop Machine Learning models for detecting financial crime risks and synthesizing data requirements.

The Customer Segment Leader will manage a team, drive business development, establish senior-level connections, and deliver revenue targets, focusing on government clients in IT.

The Customer Segment Leader for the Government Sector will manage teams and drive business development while establishing senior connections to meet revenue goals and maintain excellent service delivery.

The candidate will lead a team, develop business strategies for government clients, drive revenue, and manage stakeholder engagement.