Top Machine Learning Engineer Jobs in Mumbai

Develop and enhance quantitative portfolio construction, asset allocation, optimization, risk management, and investment decision-support models. Conduct research, back-testing, stress testing, performance attribution, and analysis of large financial datasets. Partner with investment, product, and technology teams to implement analytical capabilities, integrate research into advisor workflows, and support model governance. Apply AI and machine learning to investment processes while maintaining transparency, controls, and analytical rigor.

Execute T+1 control and AFR reconciliations, investigate and resolve discrepancies, coordinate with technology and business stakeholders, maintain reconciliation data and logic, escalate unresolved issues, and produce recurring reports. The role supports trade processing from order entry through settlement while improving operational efficiency, data quality, and process resilience.

Analyze complex datasets using advanced analytics, data mining, SQL, and visualization tools to generate actionable business insights. Partner with stakeholders and senior leaders to guide strategy, improve product performance, identify opportunities, and solve quantitative business problems. Develop data models and analytics strategies, assess data quality, identify anomalies, communicate findings clearly, and independently manage analytical programs and process improvements.

Leads regional order-to-cash product integration across order capture, fulfillment, billing, collections, cash application, and customer care. Owns business requirements, portfolio visibility, governance, BPO performance, digital adoption, and value realization. Establishes a regional community of practice, aligns requirements with enterprise roadmaps, tracks Celonis KPIs, and holds product and operations leaders accountable. Requires strong OTC, SAP, Celonis, portfolio governance, financial, analytical, and executive stakeholder-management expertise.

Build and scale machine learning systems for search, including ranking models, feature engineering, model deployment, monitoring, experimentation, and online inference. Design fault-tolerant distributed infrastructure, optimize search latency and throughput, and develop reliable ML services from proof of concept through production. Collaborate across engineering and product teams, communicate technical decisions, review code, document systems, and mentor junior engineers.

Leads AI/ML strategy, technical vision, and delivery of scalable machine learning platforms for map editing and maintenance. Drives generative AI, agentic AI, computer vision, LLM, automation, MLOps, governance, observability, security, and responsible AI adoption. Mentors data scientists, leads multidisciplinary initiatives, translates business needs into measurable solutions, and influences executives, customers, vendors, and partners.

Performs risk assessments, control testing, regulatory reporting quality reviews, remediation tracking, root-cause analysis, and process improvements for payments operations. Monitors reporting accuracy, completeness, timeliness, and control adherence while synthesizing leadership insights. Leads change initiatives and uses AI and machine learning for anomaly detection, data validation, and workflow analysis. Partners with global teams to strengthen regulatory compliance and reporting standards.

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.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Product Manager Intern will support AI-focused product development by defining and prioritizing features, documenting requirements, tracking success metrics, and coordinating with UX, research, engineering, and business stakeholders. The role includes translating technical concepts, assisting with bug triage, conducting user research and testing, analyzing product data, and presenting insights. Strong communication, curiosity, adaptability, user empathy, and interest in practical AI applications are emphasized.

Develops and owns enterprise Pega PRPC architecture, design, coding, testing, deployment, governance, and operational health. Leads technical design, rule-set governance, versioning, release management, performance optimization, integrations, upgrades, and compliance with Pega guardrails. Supports eSDLC governance, infrastructure sizing, documentation, troubleshooting, and production operations while mentoring teams and coordinating across distributed development groups.

Leads derivatives regulatory capital production, governance, controls, analytics, and remediation for SA-CCR RWA and supplementary leverage exposure. Oversees calculations, model implementation, regulatory compliance, variance attribution, control reviews, audit responses, and process improvements. Partners with Finance, Risk, Treasury, Technology, Front Office, Audit, and regulators while presenting complex derivative analytics to senior stakeholders. Requires deep knowledge of Basel III, derivatives lifecycles, counterparty credit risk, regulatory controls, and financial modeling.

Leads enterprise-wide technology strategy and architecture for complex digital and AI transformation initiatives. Advises senior executives, defines target-state architectures and multi-year roadmaps, and guides cloud, data, AI, security, application modernization, and integration strategies. Establishes architecture governance, manages risk and technical debt, oversees design integrity across workstreams, and supports implementation through adoption and value realization. Partners with delivery, engineering, product, and program teams while mentoring architects and influencing stakeholders across the organization.

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.

Build and deploy production-grade AI solutions for banking client onboarding, KYC, and document operations. Develop document intelligence models, LLM-based extraction and summarization frameworks, automation pipelines, APIs, and reusable AI modules. Collaborate with stakeholders, monitor and retrain models, analyze data, ensure privacy and governance compliance, and explore emerging AI technologies. The role may eventually expand into leadership of a specialized AI team.

Leads reporting analytics and modeling initiatives that deliver business insights to leadership. Designs dashboards, produces forecasts and reports, identifies risks, and improves performance through stakeholder engagement. Manages analytics projects and teams while ensuring data accuracy and quality. Requires advanced Excel, Power BI or Tableau, SQL, VBA, predictive analytics, data science, automation, storytelling, project management, and problem-solving skills.

Supports internal audits through data analytics, automation, continuous monitoring, and AI/ML model testing. Develops SQL and Python-based scripts, dashboards, exception reports, and automated testing routines; analyzes large datasets, validates findings, assesses controls and model performance, and communicates recommendations to audit and business stakeholders. The role also documents testing procedures, contributes to audit standards, resolves transactional issues, and partners with audit teams on complex engagements.

Designs and delivers applied AI and automation solutions for insurance, asset-management, and finance processes. Responsibilities include architecture, RAG, LLM and agentic workflows, systems integration, AI evaluation, responsible-AI controls, LLMOps/MLOps, and value measurement through analytics and process mining.

Designs and maintains ETL pipelines integrating banking-system data into analytics platforms; builds data models for reporting, analytics, and machine learning. Collaborates with analysts, data scientists, and stakeholders to define requirements and delivers insights across risk, credit, and liabilities. Ensures data quality, security, governance, and compliance while monitoring and troubleshooting workflows and infrastructure performance.

Execute internal audit plans for banking businesses and processes, including audit planning, control evaluation, issue identification, root-cause analysis, stakeholder discussions, remediation planning, and report writing. Ensure audits comply with regulatory requirements, enterprise risk frameworks, and internal policies. The role requires banking or BFSI process knowledge, audit experience, stakeholder management, regulatory awareness, and the ability to work independently. Exposure to data analytics or machine learning is advantageous.

Build and deploy analytical models for banks, NBFCs, and insurers. Develop scalable model pipelines, conduct exploratory data analysis, create feature repositories, and maintain model lifecycle configurations. Develop predictive models for credit risk and customer personalization, identify cross-sell and up-sell targets, monitor model performance, and communicate results to stakeholders to support business adoption.

Lead strategy, roadmap execution, and lifecycle management for AI-powered products including voice bots, OCR document processing, and internal AI agents. Translate business needs into platform requirements and Gherkin specifications, collaborating with engineering, data science, design, and stakeholders. Conduct market research, manage launches, optimize products, and support go-to-market planning. The role requires substantial AI product management experience, financial services knowledge, and strong technical, analytical, communication, and leadership skills.

Build and lead production AI/ML components, including embeddings, retrieval, agents, and multimodal systems, for reuse across domain teams. Translate architectural direction into technical specifications and production-ready implementations, make implementation-level decisions, identify scalability issues, and mentor engineers. The role requires deep expertise in representation learning, vector search, retrieval-augmented systems, deep learning, LLMs, agent frameworks, distributed systems, and ML engineering.

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Design, develop, and deploy production-grade AI systems using generative AI, LLMs, agentic architectures, retrieval, deep learning, and multimodal models. Build scalable representation and vector-search layers, lead experiments, optimize and evaluate models, and apply responsible AI practices. Collaborate cross-functionally to solve spatial and business challenges, mentor engineers and data scientists, and contribute to patents and publications. The role requires strong Python and ML framework expertise, production MLOps experience, and independent architectural decision-making.