Top Artificial Intelligence Jobs in Mumbai

Design, develop, and deploy production-grade generative AI and agentic systems, including RAG pipelines, autonomous agents, prompt architectures, and enterprise integrations. Build solutions using LangChain, LangGraph, vector databases, cloud services, APIs, and DevOps tooling. Advise stakeholders, document and monitor AI applications, mitigate technical and security risks, modernize RPA assets, and mentor junior developers.

Lead end-to-end payroll processing for India, including statutory filings, payments, compliance, exceptions, approvals, journals, and reconciliations. Serve as the country payroll subject matter expert, maintain data integrity, monitor KPIs and SLAs, improve processes, collaborate with cross-functional stakeholders, and mentor payroll specialists. Provide backup support to regional managers and operate effectively in a fast-paced, asynchronous international environment.

Designs and leads enterprise AI Factory and GPU infrastructure architectures spanning compute, networking, storage, Kubernetes, cloud, security, observability, and AI/ML platforms. Assesses workloads, develops reference architectures and detailed designs, performs capacity planning and technology evaluations, leads proof-of-concepts, and supports deployment, migration, troubleshooting, and production transition. Collaborates across infrastructure, security, cloud, data, application, and operations teams to ensure scalable, secure, reliable, and cost-effective AI platforms.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Lead customer and partner technical engagements focused on Microsoft Defender, Microsoft Purview, secure AI, and Copilot for Microsoft 365 readiness. Deliver security workshops, solution mapping, demonstrations, proof-of-concept support, and technical enablement across India. Translate requirements into secure Microsoft solution paths, support pre-sales and partner capability building, and collaborate with Microsoft sales and technical teams to accelerate secure AI adoption.

Leads the organization’s data science and AI roadmap, managing a hands-on team that develops statistical models, experiments, forecasting, segmentation, production AI solutions, and agentic workflows. Drives GCP-based model deployment, MLOps/LLMOps, governance, observability, ethical AI, and adoption of AI tools. Partners with business, engineering, product, BI, and analytics teams to deliver practical data products, define success metrics, and communicate technical insights to executives.

Own the Workato environment, configure enterprise integrations, and build AI-native packages using Workato AI Genie. Design and deploy agent-ready workflows and AI-powered workflow automation solutions, while managing RBAC, auditing, and multi-workspace environments. Integrate Workato with enterprise applications and support scalable workflow implementations.

Creates technical documentation, implementation guides, whitepapers, product content, landing pages, and blog posts focused on AI infrastructure and enterprise technology. Researches complex technologies, translates specifications and architectures into clear content, collaborates with technical experts, validates accuracy, and maintains consistent documentation standards for IT professionals, engineers, architects, and technology decision-makers.

Design, develop, deploy, and optimize advanced AI and machine learning models, pipelines, and agentic systems. Apply deep learning and LLMs to search, forecasting, text mining, anomaly detection, and root-cause analysis. Improve scalability, performance, and resource efficiency while working with search engines, vector databases, big data technologies, and deployment platforms.

Own the CRM Opportunities backend end to end, designing scalable data models, APIs, distributed workflows, event-driven services, and background jobs. Improve performance and reliability across large multi-tenant pipelines while handling consistency, caching, sharding, observability, and incident response. Build AI-assisted CRM features using existing LLM APIs, including context assembly, response handling, cost and latency controls, and graceful degradation. Occasionally ship Vue.js UI changes and operate services through deployment and on-call.

Drive enterprise sales growth for Akamai Cloud Infrastructure Services by developing new accounts, building strategic account plans, advising executive stakeholders, and identifying cloud, compute, and AI workload opportunities. Lead consultative cross-selling and upselling, manage complex infrastructure deals, forecast pipelines, negotiate contracts, and coordinate enterprise sales teams and technical specialists through close and adoption.

Designs and manages IAM and IGA solutions focused on securing AI agents, MCP integrations, service accounts, applications, and other non-human identities. Responsibilities include access governance, provisioning, certifications, segregation-of-duties controls, credential-less authentication, AI-aware authorization, risk remediation, incident response, compliance reporting, and stakeholder collaboration. The role also configures IGA connectors, supports AI governance initiatives, and helps automate IAM operations.

Lead ServiceNow AI/ML development, mentor engineers, and architect Agentic AI solutions. Build, train, and deploy machine learning models, NLP and LLM applications, chatbots, autonomous agents, ETL pipelines, and large-scale analytics across AWS, Azure, and Google Cloud. Work with Spark, Snowflake, databases, visualization platforms, and ServiceNow to deliver intelligent workflow automation for global enterprises.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

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.

Sell and manage solution-level offerings in industrial automation, digital twin, AI and cybersecurity. Build customer relationships, promote Yokogawa technologies across industries, and support sustainable, technical solution adoption.

Own measurement and reporting for an AI product portfolio. Responsibilities include defining metrics, running business reviews, building Superset dashboards, querying ClickHouse and Snowflake, analyzing retention and adoption, calculating AI usage economics, evaluating product quality, reconciling data sources, and informing roadmap decisions through instrumentation, experimentation, and product analytics.

Creates and scales AI-enabled digital learning content for customers, partners, and internal teams. Produces product videos, tutorials, interactive modules, onboarding journeys, certifications, assessments, and enablement assets. Uses AI and multimedia tools for scripting, editing, translation, voiceover, graphics, and content repurposing. Partners with subject matter experts to simplify technical concepts, manages Academy publishing and content quality, and uses engagement analytics to improve learning experiences and self-service adoption.

Build enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry. Develop topics, triggers, routing, fallback handling, generative answers, RAG, knowledge grounding, citations, metadata-based retrieval, multilingual capabilities, and prompts. Integrate agents with Power Automate, SharePoint, Dataverse, Teams, Azure AI Search, and Azure OpenAI or Foundry models. Support troubleshooting, security, environments, ALM, and DEV/UAT/PROD deployments.

Build and deploy production AI systems across LLM orchestration, retrieval, agents, evaluations, voice, APIs, backend services, and infrastructure. Design reliable LLM pipelines, evaluation suites, data flows, and real-time multimodal experiences. Experiment with emerging models, debug issues across the stack, and communicate technical decisions clearly while owning projects from exploration through production iteration.

Work directly with pharmaceutical customers to understand workflows and design, prototype, integrate, and deploy AI-powered solutions. Translate ambiguous requirements into technical architectures, build Python-based integrations and data workflows, lead technical discovery and implementation workshops, debug APIs and cloud deployments, and collaborate with customer and internal engineering teams. Bring recurring customer needs to product and engineering while supporting regulated enterprise AI deployments.

Build production-grade AI applications for pharmaceutical commercial, medical, and patient workflows using LLMs, retrieval, agents, structured data, and multimodal AI. Develop scientific knowledge systems, evaluation frameworks, and reliable, safe AI solutions using healthcare and life sciences datasets. Collaborate with pharma customers and domain experts to translate complex workflows into scalable technical products.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

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.