Top Artificial Intelligence Jobs in Mumbai

Design and build Generative AI solutions: benchmark and integrate LLMs, implement RAG with vector DBs, develop multimodal models and agents, drive MLOps and deployment on cloud, establish security/compliance guardrails, and support PoCs to enterprise rollouts.

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

Lead design and delivery of GenAI-enabled automation for banking KYC and operations. Own end-to-end engineering lifecycle from discovery to production, build LLM/agent prototypes, integrate securely with enterprise systems, establish engineering and governance standards, define evaluation metrics, and mentor junior staff while partnering with risk, compliance, architecture, and business stakeholders.

Lead strategy, evaluation, and implementation of AI across design, documentation, and operations. Advise leadership, develop phased adoption roadmaps, define governance and metrics, manage integration, training, and change management, and continuously refine AI workflows to boost creativity, efficiency, and quality.

Design, build, and deploy generative AI and ML solutions on cloud platforms. Develop end-to-end data pipelines, implement MLOps and CI/CD, optimize model performance, and collaborate with cross-functional teams to scale enterprise AI deployments.

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.

As an Applied Scientist, you'll develop advanced computer vision solutions using video data and Vision-Language Models, focusing on multimodal learning and real-world problem solving.

Design and execute AI/ML-specific test strategies, build automated validation frameworks, evaluate LLM outputs for accuracy and bias, test RAG pipelines and data pipelines, monitor production models for drift, and collaborate with data scientists to ensure ethical and reliable AI systems.

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.

Build and operate an AI platform to enable safe, cost-efficient deployment of LLMs and multi-agent systems. Integrate AI across the SDLC, create orchestration tooling, maintain prompt libraries and CLI tooling, instrument observability in Datadog, and design testing harnesses for non-deterministic systems while educating and consulting with engineering teams.

Design, build, and operate LLM-powered features in production. Integrate LLMs with backend services, craft prompts, optimise models, implement evaluation/monitoring, and collaborate with product, data and design teams while managing cost, privacy and reliability trade-offs.

Lead architecture and engineering for multi-agent and LLM orchestration systems. Build and maintain agentic stacks (CI/CD, testing, monitoring, deployments), create reusable agent components and prompt templates, evaluate LLM providers, mentor engineers, and produce runbooks and operational playbooks across multiple client implementations.

Lead research and survey analytics for assigned clients: develop service-line strategy, manage team, handle client escalations, prepare client reviews, drive proposals and business growth, implement quality improvements and automation, innovate service offerings, monitor competitors, and coach team members.

Lead research and survey analytics for assigned clients: create team business plans, improve performance MIS, prepare client reviews, prevent and manage escalations, develop proposals to grow accounts, drive quality improvement and automation initiatives, track competitors, and coach the team.

Lead technical architecture and hands-on development for multi-agent and LLM orchestration systems. Set engineering standards, build reusable agent components, manage CI/CD and MLOps, evaluate LLM providers, mentor engineers, collaborate on cross-timezone architecture, and produce operational documentation for agentic AI deployments across multiple client implementations.

Lead technical direction for Sovos's AI Platform and LLMOps practice: set architecture, guardrails, observability, model and prompt strategies, enable teams through workshops and tooling, and mentor engineers to scale agentic and LLM-based systems into production.

Lead design, development, evaluation, optimization, and deployment of machine learning and NLP models. Manage data preprocessing and feature engineering, integrate models into products, deploy ML models as Django web applications, and share AI/ML knowledge to improve scalability and performance.

Participate in a 14-day remote pilot using a Google Pixel 9+ as primary phone. Interact naturally with apps that generate notifications, complete daily notification-sorting tasks, submit short daily feedback, and report technical issues. Must follow instructions and maintain confidentiality via Upwork.

Remote project recruiting expert Sindhi (sd_PK) speakers to test AI chatbots in voice mode. Participants will use their smartphones to speak in Sindhi following predefined prompts, then complete written English ratings on a laptop via Upwork. Work is project-based with flexible hours, confidentiality requirements, and variable task environments. Applicants must be international freelancers and follow provided guidelines.

The AI Engineer will design and develop cognitive data solutions using AI technology, manage data pipelines, and lead cross-functional teams to deliver scalable solutions.

Design, build, and ship production-grade AI-powered full‑stack applications: React frontends, Python backends, integrate with LLMs/agentic workflows, implement CI/CD (Kubernetes/Terraform), ensure security, observability, and take prototypes to production while advising and mentoring teams.

Lead AI/ML platform engineering and operations: provide technical leadership, architect and deploy large-scale AI solutions, enable MLOps, ensure observability, security, and cloud-native best practices while mentoring teams and driving GenAI adoption.

Create animated and static content from brief to delivery across social and digital formats. Lead concepting, storyboarding, production, retouching, client presentations, and ensure brand-consistent final assets while collaborating with cross-functional teams.

Build, operate, and maintain backend services and APIs that integrate LLM/RAG features using Galaxy's AI Suite (Bedrock, AgentCore, SageMaker). Design data models, implement CI/CD and automated tests, ensure observability and reliability, optimize performance and cost, and collaborate cross-functionally to ship AI-powered products to production.

3–6 month internship supporting risk operations: monitor KRIs and case trends, assist investigations and case analysis, escalate findings, and help optimize AI/LLM tools, automation, and alerting workflows.