Top Artificial Intelligence Jobs in Mumbai

Lead the intelligent transformation of operations support functions through AI and data solutions, overseeing hands-on technical delivery and team leadership in AIOps initiatives.

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.

As an AI Engineer Manager, you will design and develop AI models, collaborate with cross-functional teams, ensure ethical AI practices, and integrate solutions into production pipelines.

Technical pre-sales architect for enterprise AI engagements. Design end-to-end AI/ML infrastructures (GPU clusters, networking, storage, MLOps, inference), whiteboard architectures, validate NVIDIA integrations, collaborate with cloud and partner teams, drive proofs-of-concept via ATC, and ensure clean handoff to delivery while supporting revenue-generating sales efforts.

Design, develop, and maintain UI and API automation for AI/ML and generative AI applications. Own end-to-end testing (functional, integration, regression), validate model outputs (accuracy, relevance, safety, hallucinations), build reusable frameworks, integrate tests into CI/CD, collaborate with engineering and product teams, and mentor junior engineers to promote automation-first AI quality practices.

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.

Operate and improve AWS-hosted SaaS environments using DevOps, automation, and AI tools. Troubleshoot production issues, automate operational tasks, support on-call, administer Windows-based web hosting (.NET/IIS/SQL Server), build CI/CD and IaC (Terraform), enhance observability, and drive responsible AI-assisted engineering to reduce toil and increase reliability.

The AI Engineer will develop, implement, and maintain AI systems and algorithms, ensuring data quality and deploying models in production. They will collaborate with teams to build data pipelines and ensure AI solutions meet requirements.

Lead design, training, and fine-tuning of large-scale ML/DL models (LLMs and generative systems) for drug discovery. Build scalable training pipelines, data pipelines, and production-ready model deployments; collaborate with engineering teams; translate research into applied AI solutions and continuously improve model performance and reproducibility.

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.

Create, review, and refine advanced mathematical problems, solutions, and proofs for AI training datasets. Design challenging problems, validate reasoning, identify errors, and produce clear technical explanations. Apply research methodology, provide structured feedback, and collaborate with project teams to improve mathematical content quality.

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.

The Network for AI Sales Specialist will develop and drive long-term sales strategies, support account managers, and engage with clients to sell complex solutions, establishing significant business relationships and meeting sales quotas.