Top Artificial Intelligence Jobs in Mumbai

Research Scientist responsible for building and advancing agent harnesses, tool use, context management, evaluations, scoring models, fine-tuning, and reinforcement learning. The role involves solving ambiguous applied AI research problems, designing experiments and evaluation infrastructure, improving LLM agent behavior, and taking research from hypothesis through production. Collaboration occurs with US-based AI leadership and an India-based applied AI team.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Leads information security governance, cyber risk management, compliance, audit readiness, client assurance, and AI governance programs. Partners with cross-functional teams, oversees regulatory and industry framework alignment, manages risk assessments and remediation, and leads a team of Cyber GRC professionals. Advises customers, regulators, auditors, and executives on security posture while supporting responsible AI adoption and emerging risk management.

Leads end-to-end power, performance, and area optimization for AI semiconductor SoCs. Influences architecture, RTL, memory hierarchies, dataflows, floorplanning, multi-voltage domains, synthesis, placement, clock-tree synthesis, routing, and power-analysis workflows. Partners with architecture, RTL, physical design, EDA vendors, and software teams to develop custom methodologies and deliver energy-efficient silicon under stringent PPA constraints.

Owns the pharmaceutical partnership pipeline, develops strategic relationships with pharma stakeholders, identifies and qualifies opportunities, and drives engagements through contract execution. Responsibilities include prospecting, CRM management, forecasting, account planning, presentations, commercial collateral, conference representation, and translating AI-enabled clinical solutions into customer value propositions. The role requires understanding disease areas, clinical workflows, and digital health solutions while collaborating on partnership strategy, negotiations, and deal execution.

Edit and optimize AI-generated education and test-prep content for accuracy, readability, traditional SEO, AI citation readiness, and product compliance. Responsibilities include fact-checking authoritative sources, updating outdated information, improving metadata, structure, internal linking, and search intent alignment, auditing published articles, enforcing style standards, managing editorial workflows, and providing feedback to improve AI-generated drafts.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Build and deploy AI-powered customer solutions across discovery, prototyping, and production. Responsibilities include designing secure multi-tenant and hybrid architectures, integrating identity and access management, encryption, networking, data engineering, CI/CD, observability, and production support. The role partners directly with enterprise customers, executive sponsors, and cross-functional teams to solve ambiguous problems, demonstrate prototypes, and standardize successful solutions. Regular domestic and occasional international travel is required.

Leads a 3–6-person AI-focused engineering team while contributing at least 50% directly to software development. Responsibilities include architecture, full-stack coding, code reviews, testing, documentation, mentoring, delivery, and collaboration with product, design, and QA teams. The role requires commercial LLM experience, strong C# and JavaScript development skills, knowledge of databases, REST APIs, microservices, cloud platforms, and software engineering management.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Leads advanced data science and machine learning initiatives for global-scale digital map-making. Defines technical direction, develops predictive and statistical models, applies AI technologies, and drives deployment across scalable big data environments. Provides technical leadership for complex projects, collaborates with engineering and architecture teams, explains predictive solutions to executives, and mentors data scientists and engineers.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Own the data marketplace product lifecycle, improving vendor onboarding, submissions, quality feedback, offerings management, and buyer discovery. Partner with engineering, research, GTM, and operations to convert complex requirements into reliable workflows. Define marketplace health and adoption metrics, use qualitative and quantitative insights to prioritize product improvements, and guide launches and iteration in a fast-growing AI startup.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Provides systems safety engineering for aircraft turbine engines and power systems. Conducts hazard, reliability, and root cause analyses; develops safety requirements, plans, procedures, and reports; reviews designs for regulatory compliance; manages requirements using DOORS; supports certification and qualification; collaborates cross-functionally on risk mitigation, reliability metrics, process automation, and AI-enabled safety improvements.

Provides technical and domain expertise during sales cycles for ServiceNow solutions. Leads discovery workshops, product demonstrations, customer and partner technical discussions, competitive positioning, and solution development. Partners with sales, product management, marketing, and professional services teams to support pipeline growth and revenue goals. Contributes product feedback, reusable best practices, and marketing event support while managing an assigned territory.

Designs and delivers secure, scalable Azure cloud and AI solutions for enterprise customers. Leads architecture engagements, proofs of concept, production deployments, and technical delivery across Azure AI, application services, GitHub, and data platforms. Ensures workload resiliency, security, optimization, and production readiness while developing reusable intellectual property. Builds RAG architectures, AI agents, and agentic workflows using open-source LLMs and modern AI frameworks. Partners with executives, engineers, and Microsoft teams to remove adoption blockers, drive consumption, provide technical workshops, and mentor others.

Lead quality assurance for agentic AI platforms, models, and cross-product workflows. Design Python and JavaScript/TypeScript automation frameworks, validate non-deterministic AI outputs, test APIs and agent pipelines, perform performance testing, and build AI evaluation and regression capabilities. Collaborate with Engineering, Product, and Data Science teams to define test strategies, monitor quality metrics, identify scalability risks, and support release readiness.

Build and deploy production LLM-powered AI and voice agents, owning systems from scoping and design through deployment, monitoring, evaluation, and iteration. Optimize prompts, RAG pipelines, and reliability frameworks while collaborating directly with customers and implementation teams to solve workflow challenges and drive successful launches.

The Engineering Specialist is responsible for frontend application development and backend integration, focusing on building scalable web applications, collaborating with teams, and adhering to architecture standards.

Own the end-to-end product experience for agentic AI products, including roadmap, feature definition, UX optimization, onboarding, and in-app education. Analyze product usage and customer feedback to identify friction, simplify workflows, improve conversion, and prioritize high-impact improvements. Partner with design and engineering while independently driving product direction, creating detailed PRDs, tickets, prototypes, and designs in a fast-moving startup environment.

Develop and deploy multilingual automatic speech recognition systems for children’s reading assessment. Responsibilities span speech-data collection and annotation, audio preprocessing, model training and fine-tuning, decoding, forced alignment, error analysis, benchmarking, and deployment in low-bandwidth or offline environments. The role requires adapting modern ASR architectures, detecting reading errors, evaluating WER/CER and alignment quality, and collaborating with assessment and field teams to translate model outputs into reading fluency and accuracy measures.

Designs secure, scalable, resilient Azure enterprise platforms and AI-ready infrastructure. Translates customer requirements into end-to-end architectures spanning networking, identity, security, governance, observability, automation, disaster recovery, and AI workloads. Leads architecture workshops, technical design, implementation guidance, governance, operational readiness, and troubleshooting. Advises customer and engineering stakeholders on cloud and AI adoption roadmaps, technical trade-offs, risks, costs, and responsible AI controls.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.