Top Artificial Intelligence Jobs in Mumbai

Design, execute, and maintain test plans for an AI-powered platform. Perform functional, integration, regression, end-to-end, API, browser/device compatibility, and performance testing. Validate AI features, prompts, and generated responses; track and verify bug fixes; collaborate with developers and product teams; prepare launch-readiness reports and recommend stability and UX improvements.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.

Design and build production AI systems for BJAK's AI Neobank: AI agents, LLM apps, RAG/retrieval, tool-calling, and automations for onboarding, KYC, risk, CRM and operations, with focus on accuracy, latency, cost, security, compliance and reliability.

The AI Engineer will develop and deploy AI/ML models and solutions, collaborate with teams, and ensure production readiness while addressing complex business problems.

Build and maintain LLM-powered applications and MCP servers, implement tool/function calling and prompt pipelines, rapidly prototype new AI tools, track AI tool releases, and deliver weekly intelligence reports with demos and recommended integrations.

The Solutions Engineer will partner with customers to help them derive business value from OpenAI's products, manage pre-sales processes, and provide technical expertise.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Create original graduate-to-PhD-level problems and rigorous step-by-step solutions in STEM fields, review AI-generated answers to identify reasoning or factual errors, and ensure clarity and verifiability. Work remotely and asynchronously through an online platform after identity verification and an initial assessment.

Execute fashion design briefs using Raspberry AI to produce flats, tech packs, colorways, prints, mood boards, and concept visuals. Translate creative direction into precise prompts, iterate on feedback, maintain brand-consistent quality, document workflows, flag platform issues, and partner with US Customer Success for intake and delivery under tight turnarounds.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and build production LLM-based agent systems, governance knowledge pipelines, hybrid retrieval and evaluation frameworks, and platform infrastructure. Implement behavioral shaping, observability, and constraints to ensure agents align with governance and compliance requirements, collaborating with product and data teams.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Provide hands-on technical support to customers deploying, operating, and scaling ArangoDB-based AI systems. Diagnose issues, optimize queries and performance, configure deployments across Kubernetes and cloud/on-prem, debug networking/storage, script automations, and document solutions while collaborating with Product and Engineering.

Design and build production AI-powered analytics: conversational interfaces, LLM agents, semantic data models, and scalable data pipelines. Partner with Product and Engineering to deploy RAG/MCP-based solutions, integrate AI with enterprise data and cloud platforms, mentor teams, and promote Responsible AI practices to improve decision-making and automation across the company.

Design and presales of end-to-end AI/HPC infrastructure: size GPU clusters, storage, networking, Kubernetes/container platforms, integrate liquid-cooled racks with facility constraints, validate solutions with OEMs, and support delivery for scalable, operable AI deployments.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Own post-sale success for enterprise customers: drive adoption, retention, and expansion of Cursor. Lead end-to-end rollouts, engage engineering leaders, convert use cases to repeatable workflows, monitor usage, and relay product feedback to influence roadmap.

Design and build agentic AI solutions and complex agent workflows using Python. Train and deploy models on AWS or other cloud, build prototypes with Streamlit/Gradio, apply MLOps, collaborate in Agile/Scrum with Git and JIRA.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Lead architecture and engineering of enterprise-scale Generative AI and data platforms. Design reusable platform services for end-to-end AI/ML lifecycle, build agentic applications and event-driven systems, enforce CI/CD and DevSecOps, ensure security/governance/observability, mentor engineers, and drive platform modernization using cloud-native, containerized, and distributed technologies.

Lead platform automation for enterprise Generative AI and data platforms: design and build IaC, CI/CD, observability, deployment automation, and self-service capabilities to accelerate AI/ML lifecycle, reliability, governance, and developer productivity across cloud-native distributed environments.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Build and maintain lab environments and CI/CD pipelines for the Skylar AI platform. Create automated end-to-end tests, simulate user journeys and telemetry, validate integrations and performance, manage versioning and rollback tests, support data generation, and document environments. Partner with product, data science, QA, and engineering to ensure reliable monthly updates and platform availability.