Top Machine Learning Engineer Jobs in Mumbai

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 the design, development, and deployment of production-ready AI and machine learning solutions using cloud AI services and generative AI. Responsibilities include building scalable pipelines, applying deep learning and neural networks, integrating models into existing systems, coordinating cross-functional teams, making technical decisions, and mentoring junior engineers.

Provides technical sales support for data and AI solutions in upstream oil and gas. Identifies customer needs, develops technical solutions, builds proofs of concept, supports demonstrations and technology launches, manages technical issues, and contributes to sales strategies and revenue targets. The role requires expertise in exploration and production workflows, data science, AI, analytics, and client relationship management.

Conduct KYC client profile reviews, validate documentation, identify risks and defects, perform root-cause analysis, report findings, and support AML compliance. Serve as a subject matter expert for stakeholders, manage remediation initiatives, contribute to process improvements and quality training, analyze PEQR and REM returns, and ensure adherence to regulations, policies, and control requirements.

Lead global KYC Operations teams and strategy, overseeing AML monitoring, governance, regulatory reporting, policy implementation, vendor coordination, training, program delivery, technology enhancements, risk controls, and continuous process improvement. Manage teams and senior stakeholder relationships while ensuring regulatory compliance, operational effectiveness, data quality, and service-level performance.

Manages KYC operations and teams supporting AML monitoring, client onboarding, account refreshes, regulatory compliance, quality reviews, reporting, and control oversight. Leads BAU and refresh programs, process enhancements, system releases, performance tracking, stakeholder coordination, and risk escalation. Ensures regulatory and documentation requirements are met while improving operational effectiveness and client onboarding quality.

Supports Citi’s KYC and AML compliance program by creating, validating, updating, and completing KYC records. Reviews corporate, ownership, management, and regulatory information; ensures documentation meets internal and local requirements; monitors workflow status and service-level targets; reports operational metrics; and escalates discrepancies, negative news, and high-risk cases. The role also coordinates with relationship management, compliance, and control teams and assists with departmental projects.

Produce and analyze market risk reporting across multiple asset classes and European and UK legal entities. Perform VaR, Expected Shortfall, sensitivities, concentration risk, and stress testing analysis; support regulatory filings, ICAAP exercises, and examinations. Maintain data quality and controls, remediate issues, monitor limits, and partner with risk, trading, technology, and legal entity teams. Build automated pipelines, anomaly detection models, regulatory commentary, and interactive dashboards using Python, Tableau, and Power BI.

Analyzes operational, credit, and fraud-risk data to identify trends, develop segmentation models, optimize strategies, and support policy decisions. Performs statistical analysis using SAS, SQL, R, Python, Hadoop, and Spark. Partners with business, governance, model risk, and implementation teams to improve decision strategies, ensure compliance, monitor risk trends, support audits, and present insights to senior management.

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.

Lead the Software Engineering Chapter for enterprise document retention and archiving platforms. Manage and develop engineering teams, set technical direction, establish engineering standards, and guide cloud-native, secure-by-design platform development. Drive CI/CD, infrastructure automation, reliability, observability, disaster recovery, security, risk management, cost optimization, and operational excellence across multiple squads. Partner with product, architecture, cybersecurity, risk, and operations stakeholders while evaluating emerging technologies, including AI-enabled solutions.

Develop credit risk scorecards, predictive models, segmentation frameworks, and portfolio analytics using commercial bureau data. Analyze MSME lending portfolios, engineer features, validate models, and generate actionable risk insights. Translate client challenges into analytical solutions, support proofs of concept and pre-sales engagements, and collaborate with product, technology, and client teams. Maintain strong standards for model governance, documentation, compliance, data security, and productionisation.

Own strategic AI-native and enterprise accounts across the full sales cycle, from pipeline generation and prospecting through closing and expansion. Partner with Solutions Architecture, Engineering, and Research teams to develop technical solutions and proof-of-concept evaluations for inference and post-training workloads. Build executive and technical relationships, manage complex sales cycles, identify customer use cases, inform product strategy, and improve sales processes.

Lead end-to-end delivery and governance of enterprise AI, Generative AI, Agentic AI, and machine learning initiatives. Manage planning, budgets, resources, risks, dependencies, stakeholders, customer engagements, and cross-functional teams including architects, data scientists, engineers, and MLOps specialists. Drive discovery workshops, roadmap definition, agile delivery, executive reporting, responsible AI practices, cloud AI implementations, and production deployment while ensuring quality and business outcomes.

Leads enterprise architecture for scalable, multi-tenant SaaS systems and cloud-native platforms. Owns AWS architecture, distributed data pipelines, cost optimization, security, governance, and architectural standards. Drives GenAI and machine learning adoption across the software development lifecycle, builds self-service capabilities, guides engineering teams, conducts prototypes, and leads architecture reviews. Requires deep hands-on cloud, distributed systems, coding, infrastructure automation, AI/ML, and strategic technology leadership experience.

Designs and delivers intelligent automation initiatives using RPA, Python, AI, cloud, and low-code technologies. Responsibilities include gathering requirements, mapping processes, assessing automation feasibility, translating needs into technical specifications, supporting testing and UAT, governing citizen development, managing Jira backlogs and Scrum ceremonies, maintaining Confluence documentation, coordinating stakeholders, reporting project metrics, and ensuring risk and compliance standards.

Build advanced credit risk models and data-driven solutions for financial services clients. Analyze bureau, alternative, and emerging data to support underwriting, decisioning, acquisition strategies, credit assessment, limit assignment, and portfolio management. Partner with business, technical, and delivery teams to define requirements and deliver client solutions. Apply machine learning, statistical modeling, and large-scale data analysis to generate commercial value and improve portfolio performance.

Develop data-driven analytics products using large datasets, Python, SQL, statistics, and machine learning. Responsibilities include exploratory analysis, feature engineering, predictive modeling, validation, benchmarking, automated workflow development, UAT support, productization, documentation, and model governance. The role collaborates with product, technology, and business teams to deliver scalable, interpretable, and reliable decision-support solutions.

Develop data-driven risk and analytics products using bureau and financial services data. Responsibilities include exploratory analysis, feature engineering, statistical and machine learning model development, validation, benchmarking, workflow automation, UAT support, productization, documentation, and collaboration with Product, Technology, and business teams. The role requires strong Python and SQL skills, reproducible coding practices, model governance, and the ability to communicate analytical insights to technical and non-technical stakeholders.

Supports research and product innovation by analyzing large-scale datasets, improving machine learning algorithms, designing experiments, preparing modeling data, developing scalable AI prototypes, and assisting with feature ideation and data quality improvements under senior guidance.

Leads commercial analytics delivery and a small functional team while driving JD Edwards-to-Oracle Fusion ERP and CRM transformation. Owns requirements, data migration, integration testing, cutover, stabilization, governance, and continuous improvement. Builds Power BI dashboards, Power Platform applications, automated workflows, data pipelines, semantic models, and applied AI solutions. Partners with business and technical stakeholders to deliver production-ready, secure, supportable solutions across global teams.

Develops and deploys machine learning models and Generative AI proofs of concept for banking operations. Responsibilities include data analysis, feature engineering, statistical modeling, evaluation framework design, model monitoring, and production integration. The role collaborates with business units, data engineers, and IT teams to deliver secure, scalable solutions and actionable insights from large banking datasets.

Drive end-to-end B2B sales and go-to-market activities for data science and applied AI solutions. Responsibilities include prospecting, customer relationship management, technical presentations, solution scoping, proposal development, business analysis, and resolving technical objections. The role partners with solution architects and data science teams to develop AI and cloud solutions, demonstrate Google Cloud integrations, manage client expectations, and identify new business opportunities across multiple industries. Travel to customer sites and events may be required.

Leads engineering strategy and delivery for payments technology, overseeing data projects, technical practices, architecture, reliability, and continuous improvement. Builds high-performing multidisciplinary teams, develops engineering culture, manages senior stakeholder relationships, and optimizes onshore and offshore resources. The role requires deep expertise in cloud platforms, databases, programming, event-driven systems, observability, security, AI, payments technology, and modern software engineering practices.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.