Top Artificial Intelligence Jobs in Mumbai

Lead corporate security operations including intelligence gathering, counter-espionage, investigations, surveillance, and community policing. Build informant networks, liaise with local law enforcement and government bodies, manage security planning and budgeting, implement security automation and AI, and train and mentor security teams.

Build and productionize ML and generative AI solutions for marketing: data pipelines, modeling, RAG/embedding workflows, automation agents, integrations, monitoring, and documentation.

Lead pre-sales solutioning and demonstrations, map customer requirements to Signzy products, create techno-commercial proposals and pitch decks, coordinate with product and delivery teams, manage client expectations, assist contract and InfoSec reviews, and support handover to project teams. Requires 2–4 years of Sales/Presales/BA/Project Management experience.

Support sales by positioning products, demoing solutions, mapping client requirements to Signzy offerings, creating proposals and pitch decks, performing business analysis and scoping, coordinating with product/project teams, managing client expectations, and handing off to delivery post-signoff. Train sales on product, compliance and technology for assigned use-cases.

The Senior Data Scientist will architect real-time fraud detection systems, process large data sets, mentor teams, communicate findings, and innovate using AI technologies for effective risk management.

Build, operate, and improve end-to-end Agentic AI/ML pipelines and map compilation platform for HD live maps. Implement scalable CI/CD/CT for ML workflows, orchestrate reliable data and pipeline systems, deploy containerized models in cloud-native environments, monitor model performance and data drift, manage ML lifecycle/versioning, automate releases and infrastructure provisioning, and collaborate on architecture and incident resolution.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Sell and manage solution-level offerings in industrial automation, digital twin, AI and cybersecurity. Build customer relationships, promote Yokogawa technologies across industries, and support sustainable, technical solution adoption.

Serve as the technical face of Platform and AI for solution sales: build POCs and accelerators, design agentic AI applications (multi-agent orchestration, RAG, prompt/context engineering), create reusable components, run discovery workshops and demos, advise on integrations, data pipelines, and security, and collaborate with sales, product, and services to scale regional capabilities.

Lead and grow an ML engineering team to design, build, and operate a unified AI/ML data collection platform. Own data ingestion, feature management, LLM/RAG workflows, model lifecycle, evaluation, observability, scalability, governance, and cross-functional alignment while mentoring engineers and driving platform reliability and operational excellence.

Partner with local and global finance stakeholders to translate business needs into data, AI, and SAP co-allocation prototypes. Drive adoption, define success metrics, deliver automated insights, and transition production solutions to owning teams while improving reporting, process automation, and decision support.

Serve as a field-facing technical authority bridging generative AI and enterprise security. Architect AI technical strategy, deliver demos and POVs, advise on LLM/RAG/agent risks and data protection, and scale enablement and product feedback across sales and engineering.

Leads global strategic sourcing and procurement for digital technology and Food & Nutrition categories. Manages $125M+ in spend, SaaS and professional services suppliers, sourcing strategies, RFPs, contracts, supplier governance, performance, risk, and cost optimization. Partners with senior stakeholders, Legal, Infosec, sourcing teams, and suppliers to support digital transformation, standardization, automation, and commercial objectives.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

Sell and position the FICO Platform to enterprise clients in consumer lending and retail banking. Build senior stakeholder relationships, uncover decision-management opportunities, collaborate with pre-sales and internal teams to design solutions, and drive long-term, profitable customer engagements. Manage quarterly and annual sales targets and travel up to 50% to engage customers across Southeast Asia and adjacent industries (fintech, telecom, media, entertainment).

Lead AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.

Lead technology vision and architecture for enterprise projects; design cloud-native, data-driven and Generative AI solutions; ensure scalability, security, governance, and engineering excellence; run architecture reviews, POCs, and implementations; evaluate emerging technologies; influence clients via workshops and executive engagements; mentor architects and distributed engineering teams.

Lead design and implementation of LLM-driven, multi-agent SDLC services and orchestration on AWS. Integrate agents with toolchains, build SDKs and data pipelines, drive AI-assisted engineering governance, mentor engineers, and ensure secure, validated AI workflows and observability.

Lead architecture for software products across cloud-native domains (AWS), drive technical governance, evaluate and adopt emerging technologies (AI/ML/LLM), produce and review secure production code, guide integration and service design, mentor teams, and influence product and operational decisions.

Design and implement Generative AI solutions to improve operational efficiency. Collaborate with teams to identify opportunities and ensure data governance.

Design and prototype technical and AI-focused online learning experiences. Partner with SMEs and vendors to produce vendor-ready curricula, modular course assets, assessments, and design systems. Drive QA, pedagogical innovation, and AI-assisted tools to measure and improve learner outcomes.

Design and deliver client solutions using agentic AI, multi-agent orchestration, RAG pipelines, LLMs, and knowledge graphs. Lead discovery, requirements gathering, BRD and HLD creation, architectural trade-off decisions, client presentations, and coordination across engineering, product, and PMO through implementation. Develop deep expertise in the QiQ contact-center analytics platform and progressively represent Solutions independently in client engagements.

Build and deploy production-grade Generative AI and LLM applications, including RAG pipelines, document processing, natural-language-to-SQL solutions, evaluation frameworks, synthetic-data pipelines, and backend APIs. Integrate vector and relational databases, optimize inference, monitor AI services, and deploy with Docker and cloud environments. Collaborate with product, backend, and domain teams to deliver secure enterprise AI solutions.

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.

Design and deliver production-ready AI/ML applications using cloud AI services and generative models. Lead and mentor teams, evaluate and adopt new technologies, and implement solutions involving deep learning, neural networks, chatbots, and image processing.