Top Artificial Intelligence Jobs in Mumbai

Provides technical leadership for FAA and EASA certification and qualification of aircraft turbine engines and auxiliary power units. Prepares and reviews certification and qualification plans, reports, and related artifacts; coordinates with aviation regulators; standardizes and improves certification processes; and applies automation and artificial intelligence to reduce cycle time and cost. Supports systems engineering activities, talent development, technical coaching, and innovation across Honeywell Aerospace programs.

Supports AI tool evaluation, governance, user testing, and adoption initiatives. The role documents capabilities and risks, tracks usage and feedback, researches AI developments, coordinates workshops and events, prepares enablement materials, and communicates findings to stakeholders. It is a functional techno-business role focused on coordination, analysis, responsible AI awareness, and business application of AI rather than coding or technical implementation.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.

Maintain and optimize production LLM and VLM services, including Flask/FastAPI microservices, Dramatiq queues, REST APIs, and Uvicorn/Gunicorn deployments. Design prompt strategies, feedback and evaluation pipelines, and AI routing integrations while monitoring latency, token usage, quality, and errors. Collaborate with engineering teams on scalable microservices, Dockerized deployments, CI/CD, security, and cloud infrastructure.

Own the middle stage of enterprise sales for MAS, from qualified conversation through close-ready opportunity. Responsibilities include client discovery, workflow analysis, stakeholder and decision-maker mapping, champion development, MEDDPICC-style qualification, cold calling, client meetings, follow-up, pricing input, senior closer preparation, and feeding market insights into product and sales strategy. The role requires consultative selling, commercial understanding, and comfort operating in an ambiguous 0-to-1 environment.

Leads end-to-end development of enterprise AI and LLM applications, including architecture, technical design, Java and Spring Boot backend development, integrations, testing, deployment, and support. Builds generative AI solutions using major LLM providers, prompt engineering, fine-tuning, and agent frameworks. Collaborates with architects and developers to translate designs into scalable microservices, while addressing security, resiliency, databases, containers, Kubernetes, and cloud platforms.

Lead and mentor 5–10 technical trainers creating and evaluating terminal-based AI training tasks. Review software, computational, electrical engineering, circuit, HDL, simulation, and automated grading tasks for correctness, reproducibility, numerical accuracy, performance, and reliability. Identify flawed assumptions, nondeterminism, race conditions, testing weaknesses, and technical inaccuracies. Allocate work, resolve blockers, provide detailed feedback, track revisions, and maintain technical documentation and quality standards.

Leads client workshops and requirements discovery, translating business needs into web applications, dashboards, metrics, and reporting solutions. Develops and maintains modern applications using AI-assisted tools, integrates data from multiple sources, performs testing, troubleshoots production issues, and manages delivery from discovery through support. The role requires strong JavaScript or TypeScript development, analytics, SQL, dashboarding, testing, cloud familiarity, stakeholder management, and client-facing consulting experience.

Lead technical sales and customer engagements for Microsoft’s AI and cloud application platforms. Deliver demos, proofs of concept, hackathons, code-with sessions, and architecture workshops. Advise developers and platform leaders on secure, scalable AI architectures, resolve technical blockers, and accelerate production deployments using Azure AI, AI Foundry, agentic AI, and cloud-native technologies. Represent Microsoft through technical thought leadership and customer relationships.

Lead technical consultant responsible for the full software development lifecycle, including technical design, implementation, testing, deployment, documentation, and support. Builds enterprise AI and LLM applications using OpenAI, Anthropic, Gemini, agent frameworks, Java, Spring Boot, Python, microservices, databases, containers, Kubernetes, and cloud platforms. Collaborates with architects and developers to translate designs into technical solutions while ensuring security, resiliency, and enterprise architecture standards.

Leads technical planning, requirements gathering, estimation, architecture, development, testing, deployment, documentation, and support for client applications. Mentors developers, provides technical leadership, collaborates with solution architects, translates designs into technical requirements, manages projects, and communicates with technical and non-technical stakeholders in a consulting environment.

Lead technical consultant responsible for designing, developing, testing, deploying, documenting, and supporting enterprise applications. The role focuses on generative AI and LLM integrations using OpenAI, Anthropic, Gemini, prompt engineering, fine-tuning, and agent frameworks. Responsibilities also include Java and Spring Boot backend development, Python, Oracle, microservices, Docker, Kubernetes, cloud platforms, security, resiliency, and enterprise architecture. Requires 8+ years of software development experience and strong AI/LLM application expertise.

Lead technical consultant responsible for designing, developing, testing, deploying, documenting, and supporting enterprise applications. The role focuses on generative AI and LLM integrations using OpenAI, Anthropic, Gemini, agent frameworks, Java, Spring Boot, Python, microservices, databases, containers, Kubernetes, and cloud platforms. The consultant collaborates with architects and developers to translate designs into technical solutions and provides expertise in security, resiliency, and enterprise architecture.

Uses AI tools, digital platforms, LinkedIn, job portals, ATS systems, and online sourcing methods to identify executive-level opportunities across the UAE and GCC. Responsibilities include tailoring applications, researching employers, connecting with recruiters, tracking applications and follow-ups, and improving recruitment workflows through automation. Requires at least three years of talent acquisition or sourcing experience, strong AI tool proficiency, excellent written English, attention to detail, and independent execution.

Own the strategy and roadmap for Netomi’s enterprise AI Agents platform. Partner with customers, engineering, research, data science, and go-to-market teams to prioritize and ship capabilities. Build agent prototypes, connect APIs and MCP tools, design evaluations, analyze model quality, debug production failures, and establish governance, observability, and rollout practices. Drive measurable outcomes including resolution rate, cost, CSAT, adoption, and revenue expansion.

Lead capture and translation of business requirements for Salesforce Sales Cloud and Experience Cloud. Partner with business leaders to define system goals, prioritize scope, drive implementations, and produce functional and technical designs for engineering teams. Support CRM evolution, agile delivery, and top-of-funnel processes like lead, account, and partner management.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Design, build, and deploy production generative AI features, including LLM applications, RAG pipelines, and agentic systems. Develop scalable Python and cloud backend services, evaluate model accuracy and performance, optimize reliability and cost, and implement monitoring, security, and observability. Collaborate with product, design, and engineering teams while mentoring teammates and exploring emerging AI technologies.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Lead and scale an AI-driven creative design team producing performance marketing assets for paid media. Own creative direction, production workflows, quality control, testing, and performance-driven iteration. Develop AI tool expertise, prompt and style libraries, and automated workflows with engineering partners. Collaborate with marketers, media buyers, copywriters, producers, and video editors while remaining hands-on with design and AI tools.

Build and deploy production AI systems, agents, APIs, backend services, data pipelines, and models. Work directly with customers to translate business challenges into technical solutions, automate workflows, run experiments, measure outcomes, and iterate quickly. The role requires strong software engineering and system design fundamentals, AI knowledge, cloud or Kubernetes familiarity, comfort with ambiguity, and strong communication skills.

Design and build deterministic, auditable AI engines and conversational agents for finance platforms. Lead architecture across RAG, agent orchestration, structured outputs, grounding, evaluation, verification, and human-in-the-loop workflows. Develop production Python services with SQL and vector retrieval, support multi-cloud and on-premises deployments, mentor engineers, conduct design reviews, and communicate architecture decisions to technical and business stakeholders.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Lead the vision, strategy, and execution of HighLevel’s Email AI platform. Responsibilities include generative email content and layout creation, autonomous lifecycle sequence generation, predictive send optimization, AI governance, privacy safeguards, roadmap development, instrumentation, and cross-functional alignment. The role aims to accelerate campaign creation, improve engagement, and launch Email AI and Sequence AI capabilities at scale.