Top Machine Learning Engineer Jobs in Mumbai

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Leads data preparation, profiling, and exploratory analysis for large-scale datasets; builds, evaluates, tunes, and deploys time-series forecasting models for demand planning. The role requires Python, SQL, AWS, SageMaker, ML frameworks, and MLOps tools. It involves independent end-to-end ownership, customer presentations, stakeholder collaboration, and translating technical work into business outcomes. Retail or e-commerce forecasting and computer vision experience are preferred. Travel to the Bengaluru client office twice weekly is required.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Develop, calibrate, monitor and automate counterparty risk models and analyses; attribute P&L to risk factors; prototype risk models; build quantitative tools; produce industry trend reports and support stress testing for reporting and event response.

Develop and deploy machine learning, deep learning, Generative AI, and LLM solutions for financial and payment data. Build fraud detection, anomaly identification, NLP, conversational AI, complaint analysis, and RAG applications. Collaborate with engineering, product, and business teams to productionize models, evaluate performance, improve scalability, and meet security, compliance, and governance requirements.

Lead the technical roadmap, architecture, development, evaluation, and productionization of machine learning and AI solutions for search relevance. Build models using queries, product catalogs, and behavioral data; integrate scalable ML components into cloud systems; analyze search quality; mentor engineers; and influence cross-functional engineering and product decisions.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture and development of scalable, secure, observable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive technical vision, software delivery, system reliability, and operational excellence while mentoring engineers. Build production platforms processing high-volume network and operational data, integrating cloud infrastructure, machine learning, telemetry, and automation capabilities.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.

Lead pre-sales activities for credit underwriting and risk assessment solutions. Partner with sales, product, and technology teams to understand lender needs, design tailored functional solutions, conduct product demonstrations, prepare proposals and documentation, address client queries, and support pilots and proof-of-concepts. Act as a subject matter expert on lending workflows, risk policies, scorecards, data sources, regulations, and automation while influencing product enhancements and stakeholder decisions.

Own end-to-end machine learning for ecommerce discovery, including recommendation and ranking models, low-latency Python serving, batch pipelines, A/B testing, and production deployments. Operate containerized services on Kubernetes, monitor systems, and extend LLM-powered shopping and catalog applications. The role requires strong recommender systems, search ranking, embeddings, vector databases, streaming, and production ML experience.

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.

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.

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.

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.

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

Leads consultative presales for credit and fraud risk solutions across Asia Pacific. Partners with clients and sales teams to discover challenges, develop proposals, business cases, platform demonstrations, executive presentations, and deal strategies. Serves as a subject matter expert across the risk lifecycle, payment ecosystems, authorization, authentication, lending, fraud, and collections. Supports go-to-market initiatives, innovation workshops, thought leadership, and proof-of-concept engagements, with travel of up to 50%.

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.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

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.

Manage strategic enterprise customer relationships, serving as the primary contact for technical support, training, issue resolution, and executive engagement. Partner with sales on account health, growth, and presales activities while advocating for customer needs internally. Apply data analytics, visualization, AI tools, and automation to improve reporting, insights, troubleshooting, and customer value. Support connectivity-focused products and regularly meet clients and attend company functions.