Top Artificial Intelligence Jobs in Mumbai

Lead end-to-end AI engineering for products and client engagements: define architecture, select stacks/LLMs, build prototypes and POCs, set AI engineering standards, ensure scalable secure deployments, mentor developers, and drive integration and documentation across cloud-native, RAG, and vector-based solutions.

Lead optimisation of cloud and enterprise AI investments by implementing FinOps practices, monitoring and reporting spend, creating cost-allocation and chargeback models, governing AI adoption, partnering with technical and business teams, providing forecasting and strategic recommendations, and driving measurable savings and accountability across the organisation.

Lead product for voice-first AI agents in regulated BFSI: own discovery, roadmap, specs, and delivery. Translate operator workflows into AI-first features, define evaluation and recovery behaviors for ASR/NLP/TTS systems, partner with engineering and customers, and ensure enterprise adoption, compliance, and measurable outcomes.

Design, build, and deploy generative AI solutions including RAG pipelines, agentic systems, and fine-tuned language models. Lead end-to-end AI projects from prototyping to cloud deployment, collaborate with cross-functional teams, and evaluate emerging AI technologies while ensuring scalable, production-grade engineering and ML ops practices.

Lead research and development of LLMs, transformers, diffusion and multimodal models. Design, train, fine-tune, benchmark, and deploy production-ready deep learning systems. Debug and optimize large-scale ML pipelines, collaborate with engineering teams, and document experiments and findings for fast-paced startup delivery.

Design and build scalable production systems and data pipelines to support GenAI and ML models. Collaborate with data scientists to integrate NLP and deep learning models into backend services, ensure code quality, security, and performance, and lead backend ownership for AI/ML projects.

Develop and deploy Generative AI solutions, engage with customers, and integrate AI/ML into cloud environments, requiring strong expertise in AI technologies and hands-on experience.

As an AI Data Engineer, you'll deploy and optimize AI models on Azure, manage upgrades, implement MLOps pipelines, and ensure compliance.

Design and develop AI-based platforms for extracting and managing data from various sources, implementing data pipelines, and collaborating with stakeholders.

Design and review multi-technology infrastructure architectures, advise clients on deployment and operations, support pre-sales, sell solutions, research emerging infrastructure technologies, coach junior consultants, and contribute to improving methodologies, processes and tools to align solutions with strategic business needs.

Build, deploy, and integrate LLM-based and AI tooling into developer workflows and CI/CD pipelines; troubleshoot and support developers; embed AI in build/upgrade/security processes; measure outcomes and iterate on tooling.

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.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Quota-carrying enterprise sales role focused on generating, qualifying, and closing AI opportunities across an assigned region. Partner with field teams and AI ecosystem partners, craft executive-quality pitch decks and proposals, manage CRM and forecasts, and translate AI technologies into business value. Drive both infrastructure/product sales and consulting engagements through the full sales cycle while embedding into account planning and co-selling motions.

Build and deploy full-stack AI applications integrating LLMs, RAG architectures, vector databases, and multi-agent systems. Develop Python backends, APIs, ingestion pipelines, responsive interfaces, and real-time dashboards. Design and optimize PostgreSQL databases, containerize applications with Docker and Kubernetes, manage AWS and GPU infrastructure, and operate Linux environments. Mentor developers, conduct demonstrations, evaluate emerging AI technologies, and improve reliability through testing, debugging, performance tuning, and quality processes.

Leads the architecture and delivery of production-grade agentic and generative AI systems, including LLM agents, RAG pipelines, multi-agent workflows, and scalable cloud applications. Guides technical architecture, production engineering, LLMOps, evaluation, observability, security, and responsible AI practices. Mentors AI/ML and backend engineers, conducts design and code reviews, and collaborates with product, data, platform, security, compliance, and executive stakeholders.

Build and deploy AI-powered solutions for enterprise and campus customers. Scope customer environments, prototype agentic AI solutions, design secure multi-tenant and hybrid architectures, and harden validated prototypes into production. Own identity, encryption, networking, CI/CD, observability, and production support for customer deployments. Partner with executives, product managers, AI specialists, analysts, and engineers while identifying reusable patterns and informing product strategy. Regular travel supports discovery, workshops, prototyping, executive readouts, and go-live engagements.

Designs and implements computer vision and machine learning algorithms for aquaculture applications. Responsibilities include developing prototypes, conducting experiments, building data-processing and training pipelines, maintaining model testing and statistical verification, extending AI codebases, documenting research, leading long-term research, recruiting talent, and tracking AI developments. The role may also involve production deployment, inference optimization, MLOps, cloud platforms, and occasional visits to aquaculture sites.

Drive process improvement, automation, and analytics across SOX, Internal Audit, and Enterprise Risk Management functions. Map workflows, eliminate manual effort, build CI/CD-enabled automation and testing frameworks, and develop Power BI dashboards using Python and SQL. Configure Jira workflows and compliance automation, implement compliance-as-code monitoring, and apply GenAI with audit-ready guardrails. Partner with technology and compliance teams to deliver scalable, measurable, and defensible solutions.

Lead the design, development, deployment, and optimization of AI-driven applications, especially RAG-based chatbots and generative AI solutions. Build scalable systems with Python and Java, deploy them on AWS and Azure, and apply machine learning, deep learning, NLP, vector databases, embeddings, and LLM integrations. Collaborate with cross-functional teams, manage technical deliverables, mentor engineers, ensure quality through testing and optimization, and adopt emerging AI technologies.

Work across growth, product, user research, and market analysis using AI tools. Run end-to-end GTM experiments, own funnel metrics, test product releases, and fix urgent operational issues. Communicate findings clearly and move quickly to ship improvements.

Design, develop, and optimize autonomous AI agents capable of reasoning, planning, tool use, and multi-step execution. Build agent frameworks, human-AI collaboration mechanisms, monitoring and evaluation systems, and production integrations. Apply large language models, prompt engineering, reinforcement learning, planning algorithms, workflow orchestration, and system design. Collaborate cross-functionally while researching advances in agent technology and improving reliability, accuracy, safety, and efficiency.

Support product operations by collecting and tracking user feedback, coordinating product issues across teams, organizing requirements, analyzing basic data, maintaining web pages, and assisting with AI-related projects. The role requires cross-functional communication, problem-solving, execution skills, bilingual English and Mandarin proficiency, and availability for 3–5 days per week for at least six months.

Support data ingestion and pipeline reliability for marketing data feeds; run and validate ML models for trend detection; test tokenisation/anonymisation for regional compliance; maintain dashboards and front-end interfaces; manage AI briefing intake and map historical campaign data; automate CSV macros and tune generative AI prompts for paid media workflows.