Top Artificial Intelligence Jobs in Mumbai

Design, develop, deploy, and optimize production-grade Generative AI and LLM-powered systems. Build agentic pipelines with orchestration, tool integration, feedback loops, asynchronous processing, and event-driven architectures. Collaborate across operations, marketing, customer experience, and product teams to identify AI opportunities. Troubleshoot systems, maintain documentation, communicate technical findings, and deliver scalable solutions using Python, cloud platforms, modern AI frameworks, and containerization.

Lead client advisory, solution design, delivery, and productization of AI-powered workflows and automation. Build GenAI solutions, integrations, document-processing systems, and secure knowledge assistants across business platforms. Develop reusable accelerators and service offerings, monitor production performance, improve accuracy and cost, and identify new client opportunities. The role requires hands-on experience with LLMs, RAG, agent frameworks, APIs, Python or TypeScript, business systems, AI security, and production deployment.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Annotate first-person industrial and commercial videos by segmenting actions, assigning timestamps and labels, and describing hands, tools, objects, and workflows. Review and correct model-generated annotations, ensure complete and consistent coverage, resolve unclear cases, and meet quality and productivity targets. Prior hands-on experience with Physical AI, robotics, or industrial egocentric video annotation is mandatory.

Own quality for AI products including a support chatbot, API Copilot, and MCP server. Build golden datasets, evaluation harnesses, scoring systems, regression coverage, and release gates. Analyze production traces and tool calls to identify hallucinations, retrieval, generation, tool-use, context, and PII failures. Define quality metrics, investigate root causes, and convert findings into actionable engineering evidence. The role requires Python, API testing, AI evaluation experience, and strong QA/SDET expertise.

Lead enterprise AI and DevOps strategy, assess systems, identify AI opportunities, and architect production-grade AI applications. Build Python backends with JavaScript or Flutter frontends, optimize LLM context and pipelines, manage data and ML lifecycles, implement CI/CD and IaC, establish AWS CloudWatch observability, and mentor engineers while communicating technical solutions to stakeholders.

Build and lead the AI engineering platform for enterprise COBOL modernization. Responsibilities include designing transformer models for COBOL-to-Java/Python translation, developing high-performance code analysis engines, optimizing inference, architecting distributed cloud-native systems, and delivering reliable AI pipelines for large enterprise codebases. The role also includes hiring and leading engineering teams, shaping product strategy, and partnering with the CEO as a technical co-founder.

Lead the design and production deployment of LLM-powered AI agents, generative AI systems, and retrieval-augmented generation infrastructure. Build and fine-tune models, develop multi-step agents, apply data science methods, and establish evaluation, monitoring, observability, and feedback systems. Partner with product, backend, and infrastructure teams to scale AI capabilities across messaging, scheduling, sales, and operations while influencing the AI roadmap and mentoring engineers.

Lead architecture for enterprise AI initiatives across products, platforms, and shared capabilities. Define AI architecture standards, reference patterns, governance, security, and operational guardrails. Guide solutions involving LLM applications, retrieval systems, agentic workflows, orchestration, evaluation, observability, and cloud-native platforms. Partner with business, product, and engineering leaders, influence strategic decisions, mentor technical teams, and support AI-enabled software development and platform modernization.

Develop and fine-tune machine learning, NLP, and deep learning models using Python and major ML frameworks. Work with transformer architectures and proprietary enterprise data, process documents using OCR and PDF parsing tools, and conduct rigorous model evaluation, statistical sampling, and error analysis.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.

Lead Voluntas’ technology strategy, architecture, AI engineering agenda, and technology team growth. Build internal systems and client-facing platforms, apply LLMs responsibly in production, establish engineering, security, privacy, and data standards, and partner with consultants, researchers, and product owners. The role also includes hiring and mentoring engineers, evaluating build-versus-buy decisions, contributing to commercial offerings, and raising AI literacy across the organization.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Own the end-to-end product experience for agentic AI products, partnering closely with the CPO, design, and engineering. Responsibilities include defining the roadmap, features, UX improvements, onboarding, in-app education, and product marketing content. The role requires deep product exploration, data-driven prioritization, identifying friction and gaps, creating comprehensive PRDs and prototypes, and shipping simple, high-impact improvements in a fast-moving startup environment.

Own the quality strategy and end-to-end testing pipeline for an AI-generated product handling real-money transactions. Build automated UI, browser, mobile, pre-merge, nightly, release, and production testing gates; integrate testing with CI/CD; evaluate external tools; and improve reliability of critical financial flows. The role partners with engineering and DevOps, uses AI coding agents daily, and drives quality initiatives as an autonomous individual contributor.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows supporting analytics, business intelligence, and AI applications. Build solutions with Databricks and cloud technologies, integrate structured and unstructured data, implement quality and governance practices, optimize architectures, and support production deployment and monitoring. Collaborate with cross-functional teams to translate business requirements into reliable technical solutions and troubleshoot complex data challenges.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Serve as a pre-sales Applied AI architect and trusted technical advisor for enterprise customers across India and APAC. Translate business needs into scalable Claude integrations, guide technical discovery through deployment, develop evaluation frameworks, deliver technical and executive presentations, and collaborate with Sales, Product, Engineering, and global teams. The role requires occasional regional travel, strong cloud architecture expertise, Python proficiency, and familiarity with LLM technologies.

Build and integrate AI agents, APIs, webhooks, databases, and business-system automations. Translate business processes and customer flows into simple, efficient workflows with minimal code. This is a remote, part-time individual-contributor consulting engagement requiring hands-on system building and immediate availability.

Oversee portfolios of Generative AI data operations projects, managing delivery, capacity planning, vendors, budgets, quality, risks, automation, and client reporting. Coach project managers and coordinators, collaborate with recruiting and finance, enforce data security and compliance, and improve processes across distributed teams.

Designs and develops scalable cloud-based and serverless software across the company’s platform. Builds prototypes and production services, applies AI throughout the SDLC, promotes modern Agile and Well-Architected practices, troubleshoots customer and internal issues, develops comprehensive test suites, and coaches software engineers. The role requires extensive distributed application experience and backend expertise across multiple programming languages, databases, cloud technologies, integrations, and workflow orchestration.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.