Top Machine Learning Jobs in India

Lead design and delivery of scalable BI and AI-driven automation for KYC operations. Build advanced Tableau dashboards, integrate ML models, ensure output governance, collaborate with engineering and business stakeholders, and mentor technical teams.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design and lead enterprise-scale AI, machine learning, and Generative AI solutions from architecture through deployment. Build data pipelines, feature engineering frameworks, RAG applications, AI agents, and LLM-powered systems. Establish MLOps practices for versioning, experimentation, monitoring, and CI/CD. Partner with stakeholders on AI initiatives, ensure security and governance compliance, and mentor ML engineers, data scientists, and AI teams.

Design and deploy production AI/ML systems, including LLM applications, RAG pipelines, automated model training, APIs, and multimodal data pipelines. Operationalize models with MLOps practices such as CI/CD, monitoring, versioning, and retraining. Build solutions on AWS using SageMaker, Lambda, S3, Glue, EKS, and Bedrock. Implement responsible AI guardrails, data governance, security, and compliance for sensitive enterprise data.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Build backend services, APIs, databases, and data pipelines for large-scale satellite imagery and geospatial analytics. Deploy and monitor self-hosted ML inference services in offline, air-gapped defense environments. Optimize batching, latency, throughput, GPU usage, observability, evaluations, and analyst-facing tooling while maintaining reproducible builds and secure deployment processes.

Build end-to-end full-stack features for air-gapped, on-premise satellite-data platforms. Responsibilities include designing APIs, schemas, queries, indexes, migrations, and data-integrity controls; creating analyst-facing interfaces with maps, imagery, dashboards, and workflows; serving tiles and dependencies locally; and debugging across the stack under limited observability. The role requires strong backend and frontend fundamentals, systematic debugging, defensive design, UX awareness, dependency discipline, and comfort with secure offline environments.

Designs, develops, optimizes, and deploys production-grade machine learning, generative AI, LLM, agentic AI, and RAG systems. Responsibilities include model and pipeline engineering, MLOps/LLMOps, cloud and enterprise integrations, evaluation, monitoring, guardrails, technical documentation, stakeholder collaboration, and mentoring engineers. The role also drives engineering standards, technical decisions, reusable frameworks, and pilots of emerging AI technologies.

Lead product strategy and roadmap for acquiring fraud and risk monitoring. Build real-time rules, risk signals, ML and AI decisioning. Reduce fraud and chargeback losses, improve recovery and operations, run experiments, monitor key metrics, and collaborate with Engineering, Data Science, Risk, Operations and Finance.

Lead the WAAP engineering product line, define AI-era API and application protection strategy, own architecture and roadmap, hire and develop engineering teams, partner with customers, ensure reliability and incident response, and collaborate across AppSec to build AI discovery, red-teaming, and AI firewall capabilities.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, and orchestration frameworks. Build scalable AI services, APIs, reusable components, and integrations with PLM, SAP, and other enterprise systems. Translate business needs into AI-driven workflow solutions, improve quality through prompt engineering and evaluation, and support the full software development lifecycle through production. Collaborate with business, IT, and engineering teams to deliver secure, reliable, cloud-native AI applications.

The Financial Services Leader will oversee the FS Consulting Practice, managing high-level strategy, client relationships, and operational delivery across several accounts while fostering innovation and cultural transformation.

Develop and maintain scalable data pipelines using Python and PySpark. Implement ETL processes, optimize performance, and ensure data quality while collaborating with cross-functional teams.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Own end-to-end quality for enterprise data products: design and implement automated testing for pipelines, transformations, APIs, and platform capabilities; validate data correctness, completeness, consistency, freshness, and lineage; build reconciliation frameworks; define data quality metrics and monitoring; create regression suites and drive root-cause analysis; and design evaluation frameworks and test strategies for AI-powered analytics agent outputs.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

Design, develop, prototype and deploy complex ML/DL and image-processing algorithms for semiconductor inspection systems. Build and train models using frameworks like TensorFlow or PyTorch, write model architectures from scratch, modify loss functions, and collaborate across teams to integrate ML solutions into products.

Develop and deploy production-grade data science, generative AI, and agentic AI solutions using Snowflake and Cortex AI. Build intelligent data-driven applications with Python, SQL, Snowpark, LLMs, RAG, prompt engineering, vector search, embeddings, and AI agents. Integrate machine learning solutions with enterprise data platforms and develop, deploy, and maintain ML models.

Design, develop, and deploy production AI/ML solutions using machine learning, LLMs, RAG, AI agents, computer vision, NLP, and predictive analytics. Prepare datasets, build APIs and enterprise integrations, monitor model performance, evaluate emerging technologies, and document architectures and deployment pipelines. Collaborate with product managers, engineers, and business stakeholders to translate operational challenges into scalable AI systems across manufacturing, supply chain, recycling, and enterprise workflows.

Design and implement test plans, write test code, validate software/SDKs and AI models for geospatial AI products. Troubleshoot issues, collaborate with data scientists, create documentation, samples, tutorials, and develop pretrained geospatial AI models for imagery, 3D, and text.

Design, develop, and deploy scalable AI/ML models and intelligent agent systems. Architect LLM-powered autonomous and multi-agent workflows using frameworks such as LangChain, AutoGPT, and CrewAI. Integrate AI solutions with enterprise systems, optimize cloud deployments, and apply MLOps and CI/CD practices. Collaborate with product, engineering, and business teams while promoting responsible AI, fairness, transparency, and knowledge sharing.

Design and implement scalable machine learning, generative AI, prediction, and optimization solutions for Nike’s corporate functions. Build and improve MLOps platforms, data pipelines, and production applications while partnering with stakeholders and technology teams. Provide technical leadership, mentor engineers, influence technical strategy, and deliver robust solutions using cloud, data engineering, and modern ML technologies.