Top Machine Learning Engineer Jobs in Mumbai

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.

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 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.

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.

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.

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.

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.

Develop analytical models, apply data mining and statistical techniques, build machine learning prediction systems and automated scores, and generate customer insights. Collaborate with business, technical consulting, and delivery teams to create client solutions and value propositions. The role requires experience handling large data volumes and hands-on use of Python, PySpark, or SAS, with banking, financial services, decisioning, or credit bureau knowledge preferred.

Supports Citi’s internal KYC program by creating, validating, updating, and completing KYC records using internal and external research. The role ensures regulatory and policy compliance, monitors workflow progress, meets service-level targets, escalates discrepancies and high-risk or negative-news cases, and reports operational metrics. It also involves collaboration with relationship management, compliance, and case-management teams, along with participation in departmental projects.

Design, build, and deploy production-grade Generative AI solutions for enterprise use cases. Responsibilities include developing RAG pipelines, prompt strategies, agentic workflows, LLM integrations, model fine-tuning, evaluation, monitoring, governance, and LLMOps/MLOps practices. The role collaborates with product, engineering, data, and business teams to deliver scalable, secure, and high-performing AI applications.

Deliver analytical consulting services, develop credit risk models using bureau and alternative data, advise on acquisition and credit-limit strategies, and translate technical findings into commercial value. The role requires advanced statistical and machine learning expertise, large-dataset analysis using Python and SAS, MSME lending knowledge, portfolio risk management experience, and the ability to manage complex projects and collaborate with business and technical teams.

Develop artist-facing and backend tools that extend SideFX Houdini and integrate with VFX and CG production pipelines. Build clean, testable, maintainable software using HOM, HDK, Python, and/or C++. Collaborate with artists, developers, and project managers across the software lifecycle, while emphasizing usability, documentation, UX, reliability, and adoption. Support procedural workflows, production tools, simulations, rendering, and distributed studio systems.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

Develop artist-facing and backend tools that extend Autodesk Maya and integrate with VFX and CG pipelines. Build reliable, maintainable software using HOM, HDK, Python, C++, MEL, and Maya APIs; improve workflows, usability, and adoption; and collaborate with artists, developers, and project managers throughout the software lifecycle. The role also involves documentation, testing, deployment, support, roadmap planning, and innovation across distributed VFX production teams.

Leads strategy, roadmap, and end-to-end development of ML-powered generative AI products. Defines requirements, prioritizes backlogs, integrates ML models into scalable experiences, tracks product metrics, evaluates emerging technologies, and coordinates cross-functional teams spanning ML research, engineering, data, UX, and creative functions.

Own the end-to-end product lifecycle for a consumer mobile app, from user research and discovery through launch and iteration. Build new features from zero, including AI-first experiences using LLMs and ML. Lead PMs or APMs and cross-functional engineering, design, and QA squads. Use customer insights and product metrics to guide decisions, while owning activation, retention, and engagement outcomes.

Lead enterprise data science solutions spanning machine learning, deep learning, Generative AI, Agentic AI, NLP, and RAG. Frame business problems, develop and evaluate models, design production AI workflows, and collaborate on deployment, monitoring, MLOps, governance, and Responsible AI. Provide technical leadership, mentor junior team members, manage stakeholder relationships, and establish data science best practices across the organization.