Top Machine Learning Engineer Jobs in Mumbai

Lead hands-on applied AI development in fintech, taking computer vision or language models from research through production and ongoing optimization. Own technical decisions across model selection, fine-tuning, prompting, cost, latency, accuracy, evaluation, data strategy, retraining, and incident resolution. Mentor engineers and convert ambiguous business problems into sequenced roadmaps. Preferred experience includes fintech applications such as KYC, fraud, risk, document intelligence, scalable GPU infrastructure, and regulated model governance.

Build and deploy end-to-end machine learning solutions, with a strong focus on computer vision, image processing, model development, feature engineering, cloud platforms, MLOps, monitoring, and CI/CD. Collaborate with stakeholders and IT teams across multiple engagements, follow Agile methodologies, ensure project delivery quality, and contribute to research and development.

Develop and integrate machine learning capabilities into an enterprise cybersecurity platform. Build models for intrusion, phishing, anomaly, and threat detection; analyze security and authentication data; engineer features; evaluate and optimize models; and integrate AI solutions through REST APIs. The role also covers UEBA, risk scoring, backend development, experimentation, documentation, and collaboration with product, engineering, and cybersecurity teams.

Develop advanced supply chain analytics solutions using descriptive, diagnostic, predictive, and prescriptive modeling, machine learning, statistical analysis, forecasting, and operations research. The role involves gathering requirements, leading projects, managing stakeholders, deploying and sustaining models with DevOps/MLOps practices, presenting insights, and mentoring analysts in R, Python, and supply chain analytics. The position requires working a 1:30 p.m. to 10:30 p.m. shift.

Partners with enterprise customers to understand requirements, design AI solution architectures, and guide production deployments. Builds integrations, prototypes, reusable tools, and technical assets while troubleshooting complex issues. Leads technical workshops, collaborates with Product and Engineering teams, and shares customer feedback to improve the platform. The role combines software engineering, customer engagement, AI deployment, and scalable solution development.

Design and deploy scalable AI solutions for enterprise customers. Build integrations, prototypes, reusable tools, and production-grade applications; lead technical workshops; troubleshoot complex issues; document best practices; and collaborate with Product and Engineering teams to improve the platform based on customer feedback.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Develop and scale enterprise generative and agentic AI platforms, including custom connectors, secure APIs, integrations, agent orchestration, monitoring, observability, and reliability capabilities. Optimize platform performance, troubleshoot complex issues, manage lifecycle activities, document architectures and best practices, and ensure security and compliance. The role also supports AI/ML pipelines, MLOps practices, cloud deployments, and technical collaboration across engineering teams.

Associate Product Manager responsible for understanding customer needs, defining product opportunities, creating requirements and roadmaps, partnering with Engineering and Design, running experiments, analyzing metrics, and improving lending product outcomes. The role collaborates with Risk, Operations, Data Science, Compliance, and external partners while independently owning product initiatives and contributing to long-term product strategy.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Manages operational relationships with member banks, resolving escalations and improving payment-processing performance. Oversees settlement, reconciliation, chargebacks, disputes, compliance follow-ups, and KPI governance across digital payment systems. Performs advanced analysis using Excel, Power BI, and SQL; prepares dashboards and leadership reports. Leads training, knowledge-sharing, process optimization, automation, and AI/ML initiatives while coordinating stakeholders and supporting regulatory responses.

Manage strategic digital payment initiatives across UPI, merchant acceptance, acquiring banks, and fintech partners. Track milestones, certifications, readiness, action items, and stakeholder commitments. Coordinate governance meetings and partner enablement while monitoring adoption, revenue, KPIs, and transaction trends. Prepare dashboards, business reports, CRM updates, presentations, and actionable insights for leadership and business teams.

Designs enterprise-scale AI, Generative AI, and Agentic AI architectures and platforms. Leads implementation of LLM, RAG, conversational AI, knowledge assistant, and multi-agent solutions; defines AI strategy, governance, observability, scalability, security, and responsible AI practices. Collaborates with stakeholders, engineers, data scientists, and delivery teams to translate business needs into AI solutions. Provides architecture reviews, customer engagement, technical leadership, and mentorship to AI engineering teams.

Responsible for selling 75F building automation and energy efficiency solutions to enterprise and channel customers. Duties include networking with clients, builders, contractors, architects, and consultants; delivering presentations and product demonstrations; preparing sales reports and forecasts; matching solutions to customer requirements; expanding strategic accounts; and supporting site surveys, audits, and project proposals. Requires extensive HVAC, building controls, and energy management sales experience across channel and enterprise models.

Designs, develops, optimizes, and deploys advanced machine learning and generative AI solutions on AWS. Builds RAG pipelines, AI agents, knowledge bases, and workflow orchestration using Bedrock and agentic AI frameworks. Responsibilities include document processing, vector search, prompt engineering, model integration, guardrails, authentication, scalable architecture, monitoring, and secure production deployment.

Leads Workday integrations and packaged application implementations, including customization, module implementation, enterprise application integration, and development using Workday Studio, EIB, Core Connector, and Workday Extend. Builds and deploys custom Workday applications, coordinates cross-functional product launches, establishes platform best practices, and supports adoption. Applies expertise in HR, payroll, and compensation while exploring AI, machine learning, and intelligent automation capabilities.

Lead Workday integrations, packaged application implementations, custom Workday Extend app development, and cross-functional product launches. Configure Workday Studio, EIB, and Core Connector solutions; manage application lifecycles through production deployment; establish platform best practices; and support adoption through team development. Apply HR, payroll, and compensation domain knowledge while exploring AI, machine learning, and intelligent automation capabilities.

Build and deploy production-grade machine learning, deep learning, and Generative AI solutions. Responsibilities include developing models, RAG pipelines, AI agents, prompt engineering, data and training pipelines, scalable APIs, and MLOps practices. The role evaluates and optimizes model performance, cost, and latency while collaborating with product, data, and engineering teams to move AI capabilities from proof of concept to production.

Design and deliver scalable Generative AI solutions using LLMs, RAG, AI agents, vector databases, and multimodal models. Lead cloud, MLOps, security, governance, and architecture strategies across AWS, Azure, and Google Cloud. Translate business needs into technical solutions, support client workshops and pre-sales, present strategies to executives, and mentor data science, ML engineering, and software teams.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Develop, train, evaluate, and deploy machine learning and deep learning models using PyTorch. Collaborate with engineering and data teams, preprocess data, write production-grade Python, monitor deployed models, analyze performance, troubleshoot issues, document experiments, and apply current ML best practices. The role may also involve cloud platforms, containerized deployment, MLOps, GPU optimization, NLP, computer vision, or recommendation systems.

Leads client workshops and requirements discovery, translating business needs into web applications, dashboards, metrics, and reporting solutions. Develops and maintains modern applications using AI-assisted tools, integrates data from multiple sources, performs testing, troubleshoots production issues, and manages delivery from discovery through support. The role requires strong JavaScript or TypeScript development, analytics, SQL, dashboarding, testing, cloud familiarity, stakeholder management, and client-facing consulting experience.