Top Artificial Intelligence Jobs in Mumbai

Develop and manage AI-enabled full-stack applications, integrating AI models and APIs, while collaborating with teams and researching new technologies.

The Technical Architect for AI/ML will define architecture for AI systems, mentor teams, and align strategies with business objectives, utilizing cloud-native tools and frameworks.

Develop responsive front-end and robust back-end systems, collaborate on AI integrations, maintain databases, and implement DevOps practices. Emphasize on security and rapid prototyping in a dynamic, startup-like environment.

The AI Platform Engineer will design and optimize AI/ML infrastructure, automate pipelines, and manage Kubernetes for effective machine learning model deployment and collaboration.

The Engineering Manager - AI Growth leads engineering for AI product adoption and growth, managing monetisation tech and collaborating across teams for product success.

Lead the QA strategy and architect automation frameworks for AI products. Design test approaches for AI models, influence teams, and drive quality initiatives.

Lead client-specific QA programs for generative AI data: define sampling and audits, run root-cause analyses and CAPAs, plan rater training/certification, maintain performance dashboards, manage escalations, standardize SOPs, pilot automations, coach teams, and ensure data compliance and security.

As a Senior AI Engineer, you will design and implement scalable AI systems, mentor junior engineers, and ensure impactful, ethical AI solutions. Responsibilities include collaborating with stakeholders and fine-tuning generative AI models.

Design, build, and deploy LLM-powered agentic and conversational AI for healthcare, including voice automation (IVR/ASR/TTS), RAG architectures, HIPAA-compliant systems, EHR integrations, developer enablement, and mentoring of junior engineers.

Design, build, and productionize scalable AI/ML pipelines and models (including LLMs and Generative AI). Deploy and optimize models across cloud and edge, implement MLOps (CI/CD, versioning, monitoring), and collaborate with data scientists and engineering teams to deliver reliable, low-latency AI solutions.

Design, develop, and deploy Generative AI applications on GCP using Vertex AI. Build LLM-powered solutions (chatbots, summarization, document intelligence), implement RAG with vector databases and embeddings, manage token usage and prompt engineering, develop backend services/APIs, fine-tune models, and monitor and scale AI workloads while ensuring performance, reliability, and security.

Assist in developing AI solutions for financial planning, working on projects involving data structures, algorithms, and integrating AI features into the platform.

Design and implement scalable Generative AI solutions. Collaborate with cross-functional teams to convert business needs into AI solutions. Oversee architecture and evaluate the performance of AI systems on cloud platforms.

Lead technical development of AI solutions for finance automation, mentor teams, integrate AI with enterprise systems, and present to executives.

The Engineering Manager will lead Generative AI teams, focusing on AI solutions for conversations, workflows, voice, support, and platform development, while mentoring engineers and driving performance and innovation.

Lead the planning and execution of test strategies, develop automated test suites for UI and API, and mentor junior team members.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

The Senior AI Engineer will design, develop, and maintain AI-driven software applications, manage projects, lead code reviews, and mentor team members while ensuring adherence to best practices in software development.

The Senior AI Engineer will design, develop, test, and maintain software applications, focusing on AI technologies while leading projects and mentoring peers.

The Senior AI Engineer will develop and maintain software solutions in a SaaS environment for life sciences, focusing on AI-powered applications and architectural decisions for data management and analytics.

The AI Tooling Program Associate will lead the development and implementation of AI tools, identify automation opportunities, and support operations within the Databricks Professional Services team.

The Senior AI QA Engineer will oversee test planning, automation framework development, API testing, and ensure high-quality AI solutions. Responsibilities include end-to-end testing across AI/ML systems, collaboration with data and DevOps teams, and mentoring junior QA members.

Build and deploy autonomous AI agents and internal copilots that research, enrich, reason, and execute GTM workflows. Implement RAG with embeddings and vector DBs, integrate agents with HubSpot and internal systems, automate data pipelines and scraping, and own agent systems end-to-end from prototype to production.

Drive data-driven decisions for Generative AI products: prepare datasets and annotation pipelines, evaluate LLM performance, analyze user and workflow data, design A/B tests, monitor cost/latency, and deliver insights to product and engineering to improve adoption and AI quality.

Provide application support and IT operations for AI and data platforms, deploy and monitor GenAI and machine learning models, troubleshoot production issues, collaborate with data scientists, and ensure system reliability and performance.