Top Artificial Intelligence Jobs in Mumbai

Join Welo Data’s Cantonese AI Trainers Network to support AI data annotation, evaluation, and prompt creation. Work remotely with flexible hours while contributing language and cultural expertise to improve AI systems. Applicants should be native or near-native Cantonese speakers living in Hong Kong, Macau, mainland China, or Malaysia, with strong English skills, digital tool proficiency, attention to detail, and curiosity.

Join Welo Data’s Vietnamese AI contributor network for potential remote projects involving text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. This is not an active opening. Projects vary in duration and compensation, with details provided before participation.

Join Welo Data’s contributor network for flexible, remote AI projects involving text, image, or audio annotation; search, ad, and chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Candidates need native or near-native Kannada fluency, English proficiency, digital tool comfort, and strong attention to detail. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Join a remote global contributor network supporting AI data projects. Depending on project needs, you may annotate content, evaluate search results or chatbot responses, create prompts, and verify cultural and linguistic accuracy. Projects range from a few days to several weeks, with scope, timelines, and compensation provided before participation.

Builds agentic AI systems, MCP servers, retrieval systems, and evaluation pipelines for reliable AI-generated code. Responsibilities include supporting large-codebase changes, implementing compile and test gates, applying concurrency and telemetry best practices, improving performance and safety, and collaborating on AI workflow experiments. The role requires Java or Python proficiency, production or developer-facing systems experience, strong communication, and a computer science foundation.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

As an AI/ML Engineer, you will develop machine learning models, collaborate with teams, preprocess data, and optimize ML pipelines. Additionally, you will mentor junior members and contribute to AI/ML strategies.

Design, build, and maintain cloud-based data infrastructure and pipelines; develop, train, and deploy AI/ML models; collaborate with cross-functional teams to productionize models, ensure data quality, scalability, and monitoring.

The Deployment Strategist role focuses on deploying AI agents in the consumer finance industry, managing customer projects from contract to go-live, ensuring operational excellence and continuous system improvement.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.

Leads AI/ML platform engineering and operational excellence across the organization. Provides technical leadership, architecture guidance, mentoring, tool and cloud-service evaluation, and enablement for AI/ML teams. Oversees model deployment, monitoring, observability, optimization, governance, security, and compliance across cloud-native and containerized environments. Translates business requirements into scalable AI/ML solutions and supports Kubernetes, serverless, MLOps, and generative AI initiatives.

Customer-facing engineer who scopes, prototypes, and deploys production-ready GenAI and agentic AI solutions. Responsibilities include end-to-end delivery, optimization, knowledge capture, and contributing to platform evolution while working onsite with customers.

Build end-to-end frontend and backend features for an AI platform: design scalable APIs, integrate ML models, build user interfaces, improve reliability and performance, and own projects from concept through deployment.

Architect end-to-end generative AI solutions centered on LLM training, fine-tuning, deployment, inference optimization, and RAG workflows. Collaborate with customers, partners, marketing, business development, and engineering teams to define tailored solutions. Lead technical workshops, training sessions, and design engagements while providing guidance on GPU-accelerated, cloud, on-premises, and scalable distributed deployments.

Build and deploy production-ready generative and agentic AI solutions directly with customers. Responsibilities include translating business needs into technical solutions, rapidly prototyping, developing multi-agent systems and RAG architectures, deploying and optimizing scalable systems, troubleshooting performance and reliability issues, documenting field learnings, and contributing code and feedback to internal AI platforms. The role requires customer engagement, technical communication, continuous learning, and willingness to work on customer premises and travel as needed.

Lead the architecture, delivery, and engineering execution of AI and automation products. Manage and mentor software engineers, establish engineering standards, build full-stack applications and AI integrations, design PostgreSQL schemas, implement secure multi-tenant systems, develop automation workflows and evaluation harnesses, and ensure observability, testing, data validation, and reliable third-party API integrations.

Leads software engineering teams delivering AI and automation products. Owns technical architecture, PostgreSQL schemas, Claude integrations, document processing, workflow orchestration, DSP APIs, secrets management, multi-tenant access control, observability, cost attribution, and AI evaluation frameworks. Builds full-stack applications with Python APIs and React, establishes engineering standards, mentors developers, manages delivery accountability, and drives reusable platform components across products.

Lead the development of AI-powered customer support solutions, guiding a team to build scalable, reliable, and secure systems using LLMs and third-party integrations.

The Partner AI Deployment Engineer will guide AWS engagements, shape technical strategy, build AI architectures, and mentor partners to drive successful AI deployments.

Lead AI/ML platform and operational engineering: design, deploy, monitor, and optimize large-scale AI solutions. Provide technical leadership, mentor teams, enforce governance, select tools, and enable cloud-native, containerized, and MLOps practices across the organization.

Own security and privacy architecture for AI/ML products and enterprise AI platforms. Embed secure-by-design and privacy-by-design controls across the AI lifecycle, perform risk and security architecture reviews, guide controls selection and implementation, ensure compliance, and lead cross-functional security initiatives and operations including SOC design and vulnerability reduction.

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.

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.

Own the architecture and delivery of production-grade generative and agentic AI systems, including LLM agents, RAG pipelines, multi-agent workflows, APIs, and cloud-native services. Establish AI engineering, evaluation, observability, guardrail, and LLMOps practices while mentoring AI/ML and backend engineers. Collaborate with Product, Data, Platform, Security, and Compliance teams to deliver scalable, secure, and business-ready solutions, and provide technical direction on GenAI strategy and architecture.