Top Machine Learning Engineer Jobs in Mumbai

Develop and deploy production-ready AI and machine learning applications using generative AI, cloud or on-premises pipelines, deep learning, neural networks, chatbots, and image processing. Collaborate with teams to improve model performance and deployment strategies, document technical designs, contribute solutions, stay current with AI advancements, and mentor junior team members.

Designs, develops, optimizes, and deploys machine learning and generative AI solutions on AWS. Builds RAG pipelines, AI agents, knowledge-base automation, vector retrieval systems, and workflow orchestration using Bedrock and agentic AI frameworks. Implements document processing, guardrails, authentication, authorization, monitoring, and scalable production infrastructure. Collaborates in Agile and DevOps environments using software engineering and CI/CD practices.

Lead and manage multiple agile engineering teams for a cloud-native SaaS platform. Own people management, strategic planning, resource and budget oversight, technical leadership, CI/CD and production quality, and stakeholder governance. Drive process improvements, risk/dependency management, and mentor team leaders while ensuring delivery of resilient, observable software with AI/ML integration.

Architect and implement enterprise-grade MLOps/LLMOps on AWS (EKS-first), building scalable pipelines for training, validation, registry, deployment, monitoring, CI/CD, observability, lineage and governance; serve as technical authority, conduct architecture reviews, troubleshoot systems, and mentor engineering teams across projects.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Design, develop, deploy, and maintain ML/AI solutions for LMS and student portals. Build recommendation engines, chatbots, predictive analytics, and data pipelines. Integrate models with web apps, support UAT, troubleshooting, documentation, and coordinate with product, tech teams, and vendors to ensure reliable, secure, and scalable ML systems.

Use data and advanced analytics to solve complex business problems, design and deploy scalable ML/data products on cloud platforms, collaborate with clients and cross-functional teams, and communicate actionable insights.

WorldQuant seeks a Quantitative Researcher to develop predictive models for financial markets, emphasizing creativity and a strong quantitative background.

Advise clients on developing and implementing data governance strategies. Design policies, frameworks, and practices for data quality, privacy, metadata management, and security. Deliver governance projects, build capability with frameworks and accelerators, and promote governance awareness across stakeholders and committees.

Lead development and improvement of RES design and universe estimation using statistical methods, machine learning, and data integration. Research and prototype new methodologies, collaborate with technology and data teams, document global data science methods, support pilots and deployments, and mentor colleagues to apply advanced analytical techniques.

The Tech Project Manager will define and execute the vision for generative AI products, manage cross-functional teams, and oversee project lifecycles to enhance competitiveness and user experience.

Design, build, and deploy production-grade Generative AI platform components across GCP/Azure. Implement RAG pipelines, multi-agent architectures, LLM gateway and execution runtimes, GenAIOps/MLOps practices, AgentOps, and platform CI/CD/infrastructure. Collaborate with implementation partners and LBU teams to deliver reusable, secure, and compliant GenAI services.

Provides technical systems management and delivery support for customer environments. Responsibilities include system configuration, troubleshooting, performance monitoring and testing, documentation, and contributing to technical solution designs. The engineer works under a technical lead, collaborates with customer representatives, and supports project deliverables in an onsite setting.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Maintain and optimize production ML/algorithm services: debug, profile and tune for CPU/memory/I/O, add regression tests, implement parallelism, build and maintain REST APIs (Django/DRF or FastAPI), benchmark scalability and costs, ensure high code quality (tests, CI/CD, docs), and collaborate with product/data/QA to meet SLAs.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

As an AI Expert, you will review and evaluate AI-generated outputs, correct text, analyze multimedia content, and ensure cultural appropriateness and accuracy in the outputs.

As a Demo Engineer, you support pre-sales by designing AI-driven demos and proof of concepts, collaborating closely with teams to showcase Cresta's capabilities.

Lead design, development, evaluation, optimization, and deployment of machine learning and NLP models. Manage data preprocessing and feature engineering, integrate models into products, deploy ML models as Django web applications, and share AI/ML knowledge to improve scalability and performance.