Top Machine Learning Engineer Jobs in Mumbai

Develop and deploy statistical, machine learning, forecasting, segmentation, classification, and regression models for Pfizer’s commercial organization. Analyze pharmaceutical datasets, support marketing mix modeling and resource allocation, build dashboards, and communicate actionable insights to marketing and commercial stakeholders. The role also explores Bayesian methods, recommendation systems, and agentic AI for decision support and workflow automation.

Leads advanced data science and machine learning initiatives for global-scale digital map-making. Defines technical direction, develops predictive and statistical models, applies AI technologies, and drives deployment across scalable big data environments. Provides technical leadership for complex projects, collaborates with engineering and architecture teams, explains predictive solutions to executives, and mentors data scientists and engineers.

Leads software engineering delivery within an agile team, designing, developing, testing, debugging, and supporting secure, scalable applications. Establishes effective and responsible AI-assisted engineering practices, including code generation, review, testing, troubleshooting, and validation. Automates recurring remediation, improves operational stability, evaluates vendor and startup technologies, and contributes to AWS web application development, Java engineering, cloud deployments, DevOps, and machine learning or LLM integrations.

Leads end-to-end delivery of AI-powered Customer Care and Order-to-Cash solutions, including Agentic AI, intelligent case management, query routing, automated resolution, and human-in-the-loop workflows. Owns project planning, functional design, testing, deployment, AI model validation, stakeholder and vendor coordination, adoption, and post-go-live support. Requires strong SAP SD, Customer EDI, OTC, Customer Care, AI/ML, cloud technology, DevOps, and project management expertise.

Lead the delivery of AI products across international markets by coordinating internal engineers, contractors, and vendors. Establish engineering standards, make technical decisions, review code, ensure testing and release readiness, and guide solutions from proof of concept through enterprise production. Oversee data, AI, generative AI, agentic AI, and full-stack applications while managing dependencies, technical debt, and delivery quality. Collaborate with product, engineering, data science, security, and global stakeholders, and evaluate emerging technologies.

Owns the end-to-end architecture, security, and integration landscape for the global o9 Enterprise Operations Planning platform. Leads integrations with SAP and non-SAP systems, evaluates hosting models, governs security and performance, manages technical risks, and supports regional rollouts. Partners with business stakeholders, vendors, DevOps, and enterprise architecture teams to translate planning requirements into scalable solutions and provide go-live leadership.

Lead full-cycle recruiting for complex engineering roles across distributed systems, cloud, security, data, and AI/ML in India. Advise engineering leaders on leveling, team design, location strategy, and market intelligence. Build diverse sourcing pipelines, conduct technically credible screens, close senior candidates, improve hiring processes, and lead talent acquisition initiatives. Mentor peers and establish recruiting standards for technical hiring.

Analyzes US credit markets, sectors, issuers, earnings, macroeconomic developments, valuations, spreads, and capital structures. Builds financial, econometric, and quantitative models; manages large datasets using Python and related tools; produces research notes, charts, and client-ready presentations. Supports strategy discussions, market positioning, investor communications, automation, and recurring publications while collaborating with strategists, economists, and global research teams.

Lead product strategy and execution for the Enterprise Data Platform's Unstructured Data domain. Define roadmap, manage end-to-end product lifecycle, partner with engineering and UX, drive adoption and go-to-market, measure performance with KPIs, and mentor product managers in a scaled agile, global environment.

Leads global payments lifecycle operations, market expansion, regulatory readiness, risk and control execution, governance, escalations, and service quality. Partners with cross-functional and senior stakeholders to deliver strategic initiatives, automation, analytics, cybersecurity, anti-fraud improvements, and client-experience enhancements. Strengthens operating models, PMO practices, procedures, controls, and talent development while overseeing performance and operational outcomes.

Leads a team of full-stack engineers while setting technical strategy, architecture, and roadmaps for enterprise portfolio design platforms. Drives delivery of scalable Python and Angular or React applications, engineering best practices, operational resilience, and Agile execution. Leads adoption of applied and generative AI, including intelligent workflows, automation, responsible AI governance, and AI-assisted development. Partners with business and technology stakeholders, mentors technical leaders, and delivers innovative solutions that improve advisor engagement, productivity, and client experiences.

Lead design and delivery of agentic AI and LLM orchestration solutions: define reference architectures, build reusable GenAI/RAG components, operationalize agentic workflows, ensure security, observability, and cross-team adoption.

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.

Lead analytics to detect and mitigate digital payment fraud across channels. Analyze large datasets, design experiments, build data-driven rules and automated processes, collaborate on fraud-fighting products, present insights to leadership, monitor trends, and measure financial impacts to reduce losses and improve customer experience.

Design and implement production-ready AI and machine learning applications using cloud AI services, generative AI, deep learning, neural networks, chatbots, and image processing. Lead technical decisions, coordinate cross-functional integration, mentor engineers, manage teams, and develop scalable ML pipelines aligned with business requirements.

Leads the sales organization by setting strategy, managing targets, recruiting and developing sales staff, building strategic client and partner relationships, monitoring performance, forecasting revenue, managing budgets, and collaborating cross-functionally to drive market expansion and business growth.

Build and deploy production-grade AI systems for portfolio companies, partnering directly with CXOs and business leaders. Translate ambiguous business problems into enterprise AI architectures, agentic workflows, integrations, data systems, models, and infrastructure. Own solutions end-to-end from discovery and architecture through implementation, deployment, iteration, and adoption. The role requires hands-on engineering, strong business communication, cross-company adaptability, and continuous evaluation of emerging AI models, tools, architectures, capabilities, and risks.

Business Analyst responsible for gathering requirements, redesigning processes, modeling workflows, supporting RPA and AI initiatives, and managing user acceptance testing. The role partners with business, technology, data, risk, and operations stakeholders to deliver transformation projects, develop business cases, improve workflows, and ensure governance, quality, compliance, and production readiness. It also promotes the use of AI-enabled tools and supports adoption of automation and continuous improvement initiatives.

The Business Analyst gathers and documents requirements, analyzes and redesigns processes, models workflows, and supports transformation, automation, and AI initiatives. Responsibilities include developing business cases, facilitating stakeholder workshops, designing future-state operating models, coordinating UAT, managing defects, supporting implementation, and ensuring governance and compliance. The role partners with business, technology, data, risk, and operations teams to deliver process improvements and AI-enabled solutions.

Business Analyst responsible for gathering requirements, designing future-state processes, identifying automation and AI opportunities, supporting RPA and intelligent automation delivery, and owning user acceptance testing. The role facilitates stakeholder workshops, creates process and business documentation, coordinates defects and readiness reporting, supports transformation delivery, and promotes governance, continuous improvement, and responsible AI adoption across financial services operations.

Analyzes business requirements, processes, and workflows; designs future-state operating models; identifies automation and AI opportunities; supports RPA and intelligent automation delivery; owns UAT planning and execution; coordinates stakeholders across business, technology, risk, and operations; and supports transformation, testing, governance, change adoption, and continuous improvement initiatives.

Leads the design, development, deployment, and integration of production-ready AI and machine learning solutions using cloud services and generative AI. Responsibilities include deep learning, neural networks, chatbots, image processing, scalable pipeline design, cross-functional collaboration, technical decision-making, team leadership, and mentoring junior engineers.

Develop novel AI and machine learning algorithms and production-ready applications for enterprise challenges. Work across computer vision, NLP, speech, multimodal, generative, and agentic AI; collaborate with responsible AI, delivery, and academic teams; publish research; develop real-world assistants and automation solutions; and mentor AI talent.

Develops analytical models, applies data mining and statistical techniques, builds machine learning prediction systems and automated scores, and generates customer insights. The role collaborates with business, technical consulting, and delivery teams to create data-driven value propositions and client solutions. Requires experience handling large data volumes and applying analytics in banking, consulting, or financial services contexts.

Develops statistical and machine learning models, applies data mining and advanced analytics to large datasets, creates prediction systems and automated scores, and delivers client-focused insights and value propositions. Collaborates with business, technical consulting, and delivery teams to define and implement analytical solutions, particularly for banking, financial services, credit decisioning, and portfolio management.