Top Machine Learning Engineer Jobs in Mumbai

Lead and manage multiple agile engineering teams for a cloud-native SaaS platform. Own people management, strategic planning, resource and budget oversight, technical leadership, CI/CD and production quality, and stakeholder governance. Drive process improvements, risk/dependency management, and mentor team leaders while ensuring delivery of resilient, observable software with AI/ML integration.

Lead and mentor a multidisciplinary engineering and data science team to design, build, and operate scalable data collection pipelines and AI/ML systems. Provide technical oversight on NLP, LLMs, generative AI, MLOps, and unstructured data processing. Collaborate cross-functionally to align solutions with business goals, ensure system reliability, security, and compliance, and drive hiring and process improvements.

Design, build, and operationalize a unified AI/ML data collection platform. Responsibilities include LLM/RAG pipelines, data ingestion, feature management, model lifecycle and deployment, observability, scalable distributed systems, and mentoring engineers to deliver production-ready ML solutions.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime use cases. Apply supervised and unsupervised methods, execute model lifecycle (feature engineering, training, validation, monitoring), support model governance, partner with MLOps for CI/CD, deliver explainable outputs, and leverage GenAI/LLMs for unstructured data extraction and summarization.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime. Perform end-to-end model lifecycle work (feature engineering, training, validation, calibration, monitoring), support model governance and audits, collaborate with SMEs and MLOps for CI/CD and productionization, deliver interpretable explainability outputs, and incorporate GenAI/LLMs for unstructured text signal extraction.

Design, develop, and deploy production AI/ML solutions for Payments. Collaborate with Operations, Product, and Technology teams, own code lifecycle, perform code reviews, ensure testing and scalability, and engage stakeholders to deliver measurable business outcomes.

Lead design, deployment, and management of prompt-based LLM models for NLP in financial services. Build data pipelines, MLOps tooling, and APIs; research prompt engineering and orchestration techniques; evaluate and optimize model performance; collaborate with cross-functional teams and communicate results to stakeholders.

The role focuses on developing analytical data science products, implementing algorithms for insights, and expertise in deep learning frameworks.

Use data and advanced analytics to solve complex business problems, design and deploy scalable ML/data products on cloud platforms, collaborate with clients and cross-functional teams, and communicate actionable insights.

WorldQuant seeks a Quantitative Researcher to develop predictive models for financial markets, emphasizing creativity and a strong quantitative background.

Advise clients on developing and implementing data governance strategies. Design policies, frameworks, and practices for data quality, privacy, metadata management, and security. Deliver governance projects, build capability with frameworks and accelerators, and promote governance awareness across stakeholders and committees.

Lead development and improvement of RES design and universe estimation using statistical methods, machine learning, and data integration. Research and prototype new methodologies, collaborate with technology and data teams, document global data science methods, support pilots and deployments, and mentor colleagues to apply advanced analytical techniques.

The Tech Project Manager will define and execute the vision for generative AI products, manage cross-functional teams, and oversee project lifecycles to enhance competitiveness and user experience.

Seeking a Senior Applied Scientist to advance AI systems for decision automation and knowledge gathering, focusing on large language models and causal reasoning.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Lead design of credit underwriting strategies and customer journeys across digital and phygital channels; perform market and portfolio analytics to identify growth and risk reduction levers; build acquisition and behavioral scorecards; design experiments for new channels; track credit risk performance and diagnostics; manage 2-3 analysts while leveraging Python, SQL, Excel, visualization and ML techniques.

As an ML Engineer, you will design, experiment, and deploy AI/ML models, evaluate their performance, and leverage Python and SQL.

Own and improve an assigned reporting and analytics domain within Revenue Operations. Translate stakeholder requirements into scalable data models and reports, manage end-to-end reporting lifecycle (data integration, modeling, report development, performance, and maintenance), integrate new data sources, promote self-service analytics, mentor junior analysts, and drive consistency and governance across global analytics platforms.

Lead the Data Analytics function, analyze large datasets, design analytics frameworks, build Tableau/Power BI dashboards, implement AI/ML predictive solutions, ensure data governance, present insights to senior leadership, and mentor data analysts.

Lead and oversee global KYC Operations team to deliver AML monitoring, governance, and regulatory reporting. Drive KYC program strategy, vendor coordination, process and technology improvements, training, project delivery, and ensure compliance with policies and regulations while managing teams and stakeholder relationships.

Bridge creative and technical teams to prototype and deliver technology-driven advertising solutions. Research and evaluate new platforms, oversee technical execution, troubleshoot issues, mentor junior technologists, and present technical concepts to clients while ensuring scalable, accessible, user-centered outcomes.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Support KYC program operations by creating and validating KYC records, sourcing internal/external data, keeping workflow tools current, meeting SLA targets, escalating high-risk findings, and assisting compliance reporting and projects.

Lead and manage a KYC operations team to deliver AML monitoring, onboarding, refresh processes, quality reviews, MIS reporting, and process improvements while liaising with Compliance and senior stakeholders to ensure regulatory adherence.

Entry-level KYC Operations Analyst assisting AML monitoring, KYC record creation and validation, interacting with relationship and compliance teams, maintaining workflow tools, meeting daily targets, escalating high-risk cases, and supporting reporting and departmental projects.