Top Artificial Intelligence Jobs in Mumbai

Lead design, implementation, and monitoring of control frameworks (CORE); drive automation, AI/ML and analytics for proactive risk identification; own RCSAs and control testing; partner with stakeholders to ensure regulatory compliance, streamline controls, and present findings to senior management.

Lead architecture for Payments Technology within GLCM, guiding technical governance, evaluating and integrating AI and emerging technologies, developing secure production code, and driving architecture standards, reuse, and operational resiliency across product families.

Lead technical design and delivery of production AI systems including representation learning, embeddings, vector search, RAG, LLMs and agentic/multimodal solutions. Architect scalable, explainable AI platforms, set best practices, evaluate emerging tech, measure business impact, mentor engineers, and contribute to research and IP.

Lead end-to-end engineering delivery of DecisionIQ: design, integrate, deploy and monitor AI/ML features; establish CI/CD, observability, and DevSecOps; partner with product and stakeholders; and build and mentor a high-performing India-based engineering team to deliver production-grade AI products.

Partner with investors to design, build, and productionize AI-powered research applications (LLM workflows, RAG, vector search, fine-tuning). Own architecture, data pipelines, backend integration, UX, monitoring, and AI safety/reliability practices from POC to production.

Lead complex AI data engineering projects to design, build, and manage relational databases and AI-ready data products. Collaborate across digital teams to run POCs, embed algorithms into production applications, and ensure robust data modeling, metadata management, and transformation pipelines for pharmaceutical clinical and scientific data.

Design, develop, and deliver AI-based technical solutions on the Aladdin platform. Build scalable services and prototypes using Python, Java, containers, and open-source AI frameworks. Work in an agile, distributed team with product managers, designers, and users to solve complex problems, optimize processes, and contribute to open-source.

Lead design and implementation of LLM-driven, multi-agent SDLC services and orchestration on AWS. Integrate agents with toolchains, build SDKs and data pipelines, drive AI-assisted engineering governance, mentor engineers, and ensure secure, validated AI workflows and observability.

Design, develop, and deploy ML models (classification, forecasting, NLP). Build scalable data pipelines, integrate AI solutions into production, and operationalize LLM and generative AI use cases. Improve model performance, ensure monitoring, governance, and lifecycle management while collaborating with Data Ops, Product, and Engineering teams.

Lead architecture for software products across cloud-native domains (AWS), drive technical governance, evaluate and adopt emerging technologies (AI/ML/LLM), produce and review secure production code, guide integration and service design, mentor teams, and influence product and operational decisions.

Lead design, development, and production deployment of AI systems combining LLMs, agentic architectures, embedding-based retrieval, multimodal models, and deep learning. Build representation and retrieval layers, scale training and inference, implement RAG and agentic workflows, apply vision-language and spatial reasoning, mentor engineers, define KPIs, and translate research into production-quality Python code using PyTorch/TensorFlow and large-scale data tooling.

Design, build, and productionize end-to-end AI-enabled full-stack software solutions. Collaborate with data scientists, product, and UX teams; implement CI/CD, MLOps, DevOps practices; maintain scalable, secure applications and perform code reviews and system troubleshooting.

Lead architecture and development of cloud‑native .NET microservices and high‑performance Web APIs. Drive AI adoption—design LLMs, agentic workflows, vector search/RAG, and AI‑augmented SDLC. Mentor senior engineers, own high‑impact technical decisions, optimise performance, and ensure secure, scalable delivery across the full SDLC in Finance/KYC domains.

Design, build, and operate end-to-end MLOps infrastructure and pipelines for training, evaluation, deployment, monitoring, and retraining. Own model-serving for batch and real-time workloads, implement CI/CD, containerized orchestration, model/data versioning, observability, and drift detection. Optimize GPU/compute utilization, enable reproducible research-to-production workflows, and evaluate emerging MLOps tools while partnering with researchers and platform teams.

Design, build, and productionize machine learning and generative AI solutions. Develop scalable data pipelines, deploy and monitor models, optimize performance, and collaborate with Product, Data Operations, and Engineering to translate business problems into reliable AI-powered applications.

Design, develop, and deploy ML models (classification, forecasting, NLP, LLMs); build scalable data pipelines; integrate AI into production; collaborate with Data Ops, Product, and Engineering; improve model performance and scalability; and implement model monitoring, governance, and lifecycle management.

Manage end-to-end technology procurement for strategic vendors: own renewals pipeline, lead RFI/RFP and negotiations, draft contracts with legal, align stakeholders, qualify suppliers, resolve post-signature delivery or commercial issues, and provide market intelligence for strategic sourcing decisions.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime use cases. Apply supervised and unsupervised methods, execute model lifecycle (feature engineering, training, validation, monitoring), support model governance, partner with MLOps for CI/CD, deliver explainable outputs, and leverage GenAI/LLMs for unstructured data extraction and summarization.

Design, develop, and maintain production ML solutions for trade surveillance and financial crime. Perform end-to-end model lifecycle work (feature engineering, training, validation, calibration, monitoring), support model governance and audits, collaborate with SMEs and MLOps for CI/CD and productionization, deliver interpretable explainability outputs, and incorporate GenAI/LLMs for unstructured text signal extraction.

Design, develop, and deploy production AI/ML solutions for Payments. Collaborate with Operations, Product, and Technology teams, own code lifecycle, perform code reviews, ensure testing and scalability, and engage stakeholders to deliver measurable business outcomes.

Lead design, deployment, and management of prompt-based LLM models for NLP in financial services. Build data pipelines, MLOps tooling, and APIs; research prompt engineering and orchestration techniques; evaluate and optimize model performance; collaborate with cross-functional teams and communicate results to stakeholders.

Lead the development of AI-powered customer support solutions, guiding a team to build scalable, reliable, and secure systems using LLMs and third-party integrations.

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.

The Account Executive drives growth by managing key accounts, identifying new business opportunities, and deepening customer relationships to enhance market share.

Manage and grow an India-based AI Development team to deliver enterprise agent applications. Translate product priorities into sprints, partner with the Director of AI on roadmap and technical execution, establish async-first delivery rhythms across US/India, handle India operations (hiring, performance, compensation), coach on modern programming practices, and unblock work through cross-team relationships.