Top Artificial Intelligence Jobs in Mumbai

The Senior Software Engineer will develop and maintain AI agents and applications, modernize platforms, and enhance product experiences using various AI tools, while following Agile methodologies.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

The Academics Manager will lead K-12 curriculum development in STEM fields, ensuring pedagogical robustness and alignment with educational standards.

As a Staff Backend Engineer, you'll design backend architecture for AI governance, ensuring auditability and compliance, while collaborating with engineering teams to enhance product capabilities.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

The role involves developing and managing integration processes using Boomi and Workato for complex business needs in finance and operations.

The Architecture Consultant delivers multi-technology consulting services, collaborates on solutions architecture, participates in feasibility studies, and engages with clients on business development.

Lead and grow a cross-functional AI platforms team to productionize LLM-powered solutions. Own platform roadmap, architecture, MLOps pipelines, vector DB integrations, security/compliance, and cross-functional stakeholder alignment to deliver scalable, reliable AI services.

Design and build production-grade Generative AI solutions: benchmark and deploy LLMs, implement RAG with vector DBs, create multimodal systems and agents, drive MLOps/CI-CD, ensure security/compliance, integrate models into microservices, and support PoCs to enterprise rollouts.

Build and maintain backend services, frontend fixes, infra, and internal tools; integrate AI features using agentic dev tools; debug services, databases, browsers, and Linux hosts; own code lifecycle from design to on-call and monitoring.

Develop, optimize, and deploy computer vision algorithms (detection, recognition, tracking) across GPUs, TPUs, FPGAs, and edge devices. Integrate and tune hardware, accelerate models with TensorRT/OpenVINO/ONNX, collaborate with cross-functional teams, and maintain production AI systems for performance, reliability, and efficiency.

Design and architect production-grade ML and GenAI solutions on Databricks, including RAG and agentic systems, MLOps pipelines, training/inference optimization, model monitoring, and customer-facing technical enablement and mentorship.

The Senior Software Engineer will lead the design and development of AI-driven solutions, mentor junior engineers, and optimize AI/ML models for various applications.

The AI Research Freelancer will design machine learning algorithms, collaborate on research projects, and develop models for educational assessments.

Lead architecture and build of an in-house agentic AI stack to replace third-party SaaS for claims adjudication, FWA detection, and provider network automation. Design backend pipelines for NHCX/FHIR, develop scalable distributed systems, deploy Vertex AI/Gemini models and agentic workflows, and deliver real-time risk scoring and provider trust signals while working onsite in Mumbai.

Lead design and delivery of a GCP-native, agentic AI insurance core replacing SaaS systems across product, pricing, quoting, underwriting, and policy accounting. Build multi-agent pipelines, RAG/LLM solutions, and secure, auditable architectures handling PHI/SPDI while integrating national healthcare exchanges.

Lead design and build of production-grade LLM/agent applications, architect scalable systems, implement MLOps, mentor engineers, and operate cloud-based deployments (Azure preferred).

Lead design and delivery of production-grade LLM-powered applications and agent orchestration. Build scalable agent systems (LangChain/LangGraph/AutoGen), implement MLOps for serving, monitoring and versioning, manage cloud infra (Azure, containers, serverless), design distributed APIs, mentor engineers, and produce technical documentation for cross-timezone collaboration.

Lead development and implementation of the EisnerAI Platform; design scalable AI/agent solutions, ensure data governance and responsible AI, mentor developers, perform model evaluation and post-training optimization, and coordinate cross-functional delivery.

As a Vice President Data Analyst at Citi, you will lead a team in developing advanced data analytics solutions, integrating AI/ML strategies for risk management, while ensuring data governance and delivering impactful presentations to stakeholders.

Design and build AI-powered SOC automation: develop LLM/RAG solutions, NLP/ML models for log parsing and alert triage, integrate AI with SIEM/SOAR and ticketing, maintain CI/CD and MLOps, and collaborate with SOC and engineering teams.

An App Engineer will build AI-powered automations and workflows, improve productivity, integrate systems, and collaborate globally to deliver solutions.

Design and build AI-powered application security solutions: automated SAST/DAST/SCA/IAST tooling, LLM-based code analysis and RAG pipelines, CI/CD integration, secrets detection, vulnerability lifecycle automation, MLOps for model deployment, and developer-focused security tooling and documentation.

Design and build AI-powered DevSecOps automation: integrate security into CI/CD, develop ML/LLM solutions (RAG, prompt engineering), automations, APIs, container/Kubernetes security, MLOps pipelines, event-driven processing, integrations with SIEM/SOAR and ticketing, and monitor model/automation performance.