Top Machine Learning Jobs in India

Lead a team building user-facing product features that leverage the ML platform. Design scalable, reliable frameworks and APIs, develop full-stack features, resolve production issues, ensure performance and security, and collaborate across teams while participating in code and design reviews.

Lead a Core Platform engineering team to design and build scalable, reliable backend and API product features that integrate with ML systems. Drive service-oriented architecture evolution, implement frameworks for rapid feature development and deployment, resolve production issues, participate in code/design reviews, and support continuous delivery in a high-concurrency environment.

Lead and grow a data science team to deliver predictive and ML-driven insights. Partner with business and engineering to build production-ready models, enforce model governance, ensure explainability, and measure business impact while driving continuous improvement.

Lead design and delivery of secure, scalable enterprise software. Write, review, and debug production code; drive system design, testing, and operational stability. Champion AI-assisted engineering practices, vendor evaluations, and cross-team communities of practice. Promote secure, resilient CI/CD and cloud-native development while coaching teams on responsible AI adoption and automation to improve code quality and delivery.

Design, build, and maintain backend distributed infrastructure and microservices supporting ML, frontend, and platform teams. Improve scalability, performance, observability, and deliver end-to-end features like config management, rate limiting, feature flags, A/B testing, and traffic replay while coordinating across teams.

Lead the experience vision and strategy for Expedia Group's protection and FinTech platform. Simplify complex coverage, pricing, eligibility, and claims flows; define service architecture, responsible AI personalization, design systems, and cross-brand UX patterns. Drive end-to-end UX from discovery through delivery, conduct mixed-methods research and experimentation, create high-fidelity prototypes and specifications, and partner with product, engineering, data, legal, and partners to deliver scalable, compliant, high-trust experiences.

Design and deliver production-grade GenAI systems and agents for Telecom OSS/BSS: build RAG pipelines, train and optimize LLM/SLM models, deploy scalable Python services/APIs, and integrate cloud-native ML solutions to drive automation, intelligence, and decisioning at scale.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

The Engineering Lead will design and implement technical features, work on ML pipelines, and mentor engineers while ensuring high-quality deliverables. This role requires collaborating with teams and managing the ML lifecycle for business value.

Build end-to-end internal audit applications and dashboards to automate control testing and continuous risk monitoring. Integrate data pipelines, LLMs/ML, and cloud-native microservices to deliver near real-time risk insights and partner with auditors and data engineers on telemetry and deployments.

Lead architecture, development, deployment, and scaling of enterprise-grade ML/AI solutions. Drive technical strategy, MLOps/DevSecOps practices, cloud and on-prem ML platforms, mentor data scientists, partner with senior leaders to translate business goals into measurable ML outcomes, and champion innovation and best practices across teams.

Design, build, and maintain backend distributed systems and core infrastructure services (configuration, rate limiting, feature flags, A/B testing). Improve performance, scalability, and observability of cloud infrastructure. Deliver cross-team, deadline-sensitive features end-to-end and influence the infrastructure roadmap.

Lead technical design and build scalable data pipelines and platforms to support AI Ops, SRE, and analytics. Set standards for quality, security, and observability, mentor engineers, operationalize models, and contribute to roadmap and documentation for the ONE Data Lake.

Design, build, and maintain SQL queries, Databricks pipelines, Alteryx and Python automations to produce governed self-service analytics in ThoughtSpot. Optimize performance, document data models and lineage, enforce data governance, deliver production-grade dashboards and recurring MI, translate stakeholder needs, lead complex analytics projects, and mentor team members.

Develop and deploy digital twins, multiphysics and structural models, and physics-informed AI to predict and optimize semiconductor process-structure-performance interactions. Apply computational materials science, ML, GenAI/LLMs, and sparse-data methods to accelerate technology development, yield, and reliability. Collaborate with global engineering teams to translate modeling insights into fab-technology co-optimization.

Lead an agile team to design, develop, and deliver secure, resilient full-stack applications. Produce high-quality Java/React code, drive CI/CD and automation, adopt AI-assisted engineering practices, evaluate architectures, and improve operational stability and testing.

Lead equipment engineering for assembly manufacturing to improve safety, quality, reliability, productivity and cost. Drive equipment strategy, lifecycle management, automation, Lean/Six Sigma improvements, and AI-enabled predictive maintenance and analytics. Build team capability, deploy Industry 4.0 solutions, and partner across functions to meet OEE, downtime, and productivity targets.

Responsible for starting up, developing and optimizing assembly/process equipment to improve yield, quality, cost and productivity. Diagnose and resolve manufacturing issues using FMEA, SPC, DOE and failure analysis. Lead yield improvement, supplier/material management, process qualifications and integrate AI/data-driven methods for predictive control, anomaly detection and analytics-driven decision support.

Validate, document, and challenge quantitative models across mortgage, credit, and market risk. Perform complex statistical analyses, forecast losses and capital, develop/benchmark models, and communicate findings to stakeholders, auditors, and regulators. Produce detailed validation reports and support special projects including CCAR.

Design and implement technical features, collaborate with clients, write production-ready code, ensure quality deliverables, mentor team members, and implement ML models on large scale.

Lead plant digital transformation by running pilot projects, evaluating disruptive technologies, partnering with startups, implementing MES/IIoT, and owning end-to-end product management and adoption to improve efficiency, quality, and sustainability.

The Sr. Security Engineer will design advanced DLP solutions, implement AI data classification, and create protection standards for sensitive IP within the organization.

Design, build, and deploy analytics, ML and AI solutions using SQL, Python, Databricks, MLflow, and Azure. Create dashboards and reports in Power BI, perform EDA, forecasting, and model development, and collaborate with stakeholders to translate insights into actionable business recommendations while ensuring responsible AI use.

Develop and validate process, structure, and materials models using numerical methods and machine learning to enable predictive and prescriptive analytics for memory development and manufacturing. Implement and code multi-physics and process models, analyze results, present findings, and collaborate with cross-functional teams to drive decision support and automation.

Serve as primary customer contact for FourKites' SaaS platform: troubleshoot, replicate, and track product issues; coordinate with Development, QA, and Consulting; provide regular client updates; participate in weekend emergency rotation; and contribute to AI-driven support initiatives.