Top Artificial Intelligence Jobs in Mumbai

Develop and deploy production-ready AI and machine learning applications using generative AI, cloud or on-premises pipelines, deep learning, neural networks, chatbots, and image processing. Collaborate with teams to improve model performance and deployment strategies, document technical designs, contribute solutions, stay current with AI advancements, and mentor junior team members.

Serve as a pre-sales technical advisor for enterprise customers across India and APAC. Architect scalable Claude and LLM solutions, guide technical discovery through deployment, develop evaluation frameworks, deliver technical presentations, and collaborate with Sales, Product, and Engineering teams. The role requires customer-facing architecture expertise, cloud integration experience, Python proficiency, familiarity with LLM tools, and occasional regional travel.

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.

Lead and evolve solution architecture across cloud, backend, and frontend. Design scalable, secure AWS architectures, guide Java/Spring Boot microservices, advise on AI integrations, mentor teams, drive cloud optimization, and act as primary technical client contact.

Serve as a customer-focused cloud solution architect for AI-enabled Microsoft platforms. Translate customer needs into scalable Azure architectures, drive adoption, mitigate risks, guide partners, deliver proof-of-value, mentor teams, and shape product and partner strategies to increase consumption and business outcomes.

As a Staff Engineer, you will design and build cloud-native systems using Python and microservices architecture, focusing on AI-native solutions and data pipelines while collaborating cross-functionally.

Build and deploy AI applications using LLMs, prompt engineering, embeddings, vector databases, and RAG. Develop lightweight machine learning models, integrate AI into APIs and business workflows, optimize quality, cost, and latency, and deliver rapid proofs of concept. Collaborate with business and engineering teams to solve practical problems while evaluating emerging AI tools and techniques.

Build and deploy AI applications using LLMs, prompt engineering, embeddings, vector databases, and RAG. Develop lightweight machine learning models, integrate AI into APIs and workflows, evaluate and optimize model quality, cost, and latency, and deliver rapid proofs of concept. Collaborate with business and engineering teams to solve ambiguous problems while experimenting with emerging AI tools and techniques.

Lead end-to-end client implementations of GreyLabs AI speech-analytics platform, owning POC through onboarding. Drive client interactions, translate needs into project plans and playbooks, manage integrations with client systems, validate delivery quality, track milestones and risks, and influence cross-functional teams to ensure successful outcomes.

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.

Build and deploy production-ready generative and agentic AI solutions for customers. Responsibilities include customer engagement, rapid prototyping, multi-agent orchestration, RAG implementation, end-to-end deployment, performance optimization, security, documenting deployment insights, and contributing code and feedback to AI platforms. The role combines engineering, consulting, and product collaboration, often working directly on customer premises.

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.

The Tech Project Manager will define and execute the vision for generative AI products, manage cross-functional teams, and oversee project lifecycles to enhance competitiveness and user experience.

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.

Provides technical systems management and delivery support for customer environments. Responsibilities include system configuration, troubleshooting, performance monitoring and testing, documentation, and contributing to technical solution designs. The engineer works under a technical lead, collaborates with customer representatives, and supports project deliverables in an onsite setting.

Lead design, architecture, and delivery of enterprise Generative AI, LLM, and automation solutions for Institutional Securities. Translate business problems into product roadmaps, define technical designs, manage Agile delivery, build GenAI copilots and AI agents, collaborate cross-functionally, apply design thinking, measure KPIs, and present solutions to senior leadership.

Lead architecture and delivery of enterprise-scale data platforms and AI-enabled finance solutions. Drive GenAI/LLM integration (RAG, orchestration, agents), natural-language data access, evaluation frameworks, governance, and team leadership to enable secure, scalable analytics and AI-assisted workflows across finance technology.

Support Partners by leading proposal, RFP/RFI and solution document development; customize client materials; conduct research; manage proposal library and intellectual capital; maintain templates and quality standards; develop AI-enabled proposal workflows using ChatGPT, Claude, Copilot and Gemini; track proposal performance and coordinate across Partners.

Design and build intelligent AI agents and end-to-end automated workflows using Azure AI Foundry for tax, compliance, document processing, and M&A. Implement post-training techniques (fine-tuning, instruction tuning, RLHF), integrate agents with enterprise systems, and design evaluation frameworks for accuracy, reliability, and compliance.

Customer-facing technical sales role designing and validating AI-native, cloud-native application architectures; running workshops, hackathons, POCs; accelerating Azure AI and application platform adoption and consumption; and collaborating with account teams, specialists, and partners to drive technical decisions and pipeline progression.

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.