Top Machine Learning Jobs in India

Manage pre- and post-trade lifecycle operations across asset classes, including reconciliations, cash coordination, onboarding, market and product expansion, and service enhancements. Coordinate Operations, Technology, Data Analytics, Risk, Compliance, and business stakeholders. Lead governance forums, track commitments and milestones, provide executive reporting, manage change, and drive automation, standardization, scalability, and operational excellence.

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.

Manage and verify client data for KYC and AML compliance, support client onboarding, resolve service issues, track exceptions, and collaborate with stakeholders. Use digital platforms and analytical tools to improve data processes, maintain confidential information, meet regulatory requirements, and drive operational efficiency through process improvements and data-driven initiatives.

Lead experience strategy for Expedia Group's protection and FinTech platform, simplify complex coverage and claims, define UX principles and systems, design responsible AI personalization, create high-fidelity prototypes, run mixed-methods research, partner with cross-functional teams, and mentor designers to deliver scalable, compliant, high-trust experiences.

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.

Executes AI security governance operations, including compliance monitoring, control evidence validation, risk and exception escalation, alert triage, incident support, audit documentation, and remediation coordination. The role tracks AI registrations, approvals, lifecycle status, use cases, telemetry, policy violations, and control gaps while collaborating with engineering, identity, data protection, vendor risk, and Responsible AI teams.

Develop and deploy machine learning, deep learning, and generative AI solutions using LLMs, LangChain, fine-tuning, and RAG. Build traditional ML models with XGBoost and K-means, write production-quality Python code, and use Docker, Git, and terminal-based workflows. Responsibilities include testing, documentation, deployment, and maintaining clean, efficient, PEP8-compliant code.

Lead full-chip floor planning, custom analog and mixed-signal layout, physical implementation, verification signoff, and tape-out for advanced memory and high-speed interface designs. Own DRC, LVS, ERC, EMIR, reliability, and manufacturability closure. Define scalable layout methodologies, drive AI-enabled design automation, lead global cross-functional teams, mentor engineers, and deliver advanced-node semiconductor products.

Leads analytics for Wise’s financial crime and risk controls teams, turning complex data into actionable insights, metrics, reports, visualizations, and product recommendations. Partners with product managers, data scientists, engineers, and operations teams to detect and mitigate financial crime, balance risk with customer experience, and identify new control opportunities. Manages and develops a team of three analysts while maintaining analytical quality and prioritization.

Lead development of machine learning and analytics solutions for global map-making. Design scalable Big Data processes, build and validate predictive models, apply computer vision and point-cloud techniques, work with Hadoop/Spark pipelines, deploy and maintain applications, and lead predictive modeling teams while communicating results to technical and non-technical stakeholders.

Manage verification for complex DRAM design projects, develop methodologies, lead teams, provide technical advice, and enhance AI-driven verification processes.

Owns product quality frameworks and governance for ESG products across the product lifecycle. Defines quality standards, KPIs, dashboards, controls, and assurance plans; validates features, releases, and methodologies; monitors performance; leads root cause analysis and corrective actions; and drives automation, AI-enabled quality controls, process improvement, and cross-functional transformation with Product, Research, Technology, Data Operations, and Client Services.

Lead enterprise production GenAI and machine learning initiatives for financial crime, risk, and compliance. Own the full model lifecycle, including data controls, feature engineering, validation, explainability, monitoring, retraining, and model risk governance. Build MLOps infrastructure and partner with compliance, investigations, operations, technology, audit, and senior leadership to develop defensible detection and decisioning solutions. Mentor junior data scientists and drive applied research in anomaly detection, graph analytics, weak supervision, and related methods.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Lead AML-focused business analysis and product delivery: develop strategy, translate analytics into products, manage multiple projects, partner with Tech/Model Risk, mentor analysts, and communicate results to senior stakeholders.

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.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Design and deploy production-grade ML and GenAI solutions (LLMs, RAG, AI Agents). Align models to business goals, build scalable ML systems, validate models with evaluation frameworks, mitigate bias, integrate GenAI with enterprise platforms, and collaborate cross-functionally to drive data-driven decisions.

Lead and own the Data Science and AI function, driving analytics strategy, stakeholder engagement, portfolio outcomes, team leadership, technical design, and compliance with data policies to deliver measurable business value.

Lead fraud risk analyst who builds early-warning systems, detects transactional anomalies, analyzes merchant behavior, designs monitoring strategies, partners cross-functionally, maintains queries and visualizations, and takes action to mitigate fraud and credit risk.

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.

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.

Design, build, and operate observability, anomaly detection, and reconciliation tooling for monetization systems. Implement metrics/logs/traces, automated billing and data anomaly detection, SLOs, runbooks, incident response, and collaborate with product, finance, and support. Leverage telemetry and machine learning to predict and resolve monetization issues, and guide technical direction for reliability and detection work.

Design, validate, and monitor card fraud strategies using data analysis and analytics/AI. Partner with product, technology, and operations to develop, test and implement detection and treatment capabilities, balancing fraud mitigation with revenue, costs, and customer impact.

Perform market-microstructure and algorithmic execution research: develop mathematical models for single-stock and portfolio execution, order placement and routing, build quantitative tools for order-flow analysis, optimize execution performance, and publish white papers to translate research into trading improvements.