Top Artificial Intelligence Jobs in Pune

Owns Sensata’s commercial AI product portfolio across sales, revenue operations, pricing, marketing, and finance. Maps workflows, identifies pain points, defines product requirements, prioritizes roadmaps, and manages backlogs. Leads pilots through production, user adoption, governance, vendor integration, and value measurement. Builds business cases, tracks revenue and productivity outcomes, and communicates product decisions to senior leaders while partnering with AI engineers, data scientists, IT, Finance, and business stakeholders.

Leads the development and operation of Sensata’s enterprise AI platform on Azure, including shared AI services, infrastructure, CI/CD, MLOps/LLMOps, security controls, monitoring, and reusable developer tools. Owns production readiness, platform governance, reliability, cost optimization, and roadmap execution while leading platform engineers and coordinating delivery across global teams.

Lead end-to-end accounts receivable activities, including collections, cash receipts, cash application, reconciliations, refunds, subscription management, pricing updates, patient claim submissions, and reserve write-offs. The role supports order-to-cash optimization, accurate financial documentation, dispute resolution, audit compliance, and cross-functional coordination with Finance, Legal, Sales Operations, and Customer Success teams.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Design and scale cloud and Kubernetes infrastructure, distributed systems, CI/CD, observability, and deployment tooling. Build production-grade AI agents for infrastructure and engineering workflows, including tool use, evaluation, permissions, guardrails, and failure handling. Improve reliability, performance, and cloud efficiency; troubleshoot across application and infrastructure layers; mentor engineers; document architecture; and participate in on-call operations.

Own end-to-end AI data engineering solutions, including architecture, ingestion, transformation, processing, metadata, quality, lineage, observability, and production operations. Build scalable pipelines and AI-ready data assets using Python, Kubernetes, Spark, PostgreSQL, Elasticsearch, and Microsoft Fabric. Develop semantic ingestion, retrieval, and LLM-assisted processing capabilities while enabling AI and agentic applications. Collaborate with researchers, standards experts, and patenting stakeholders, and provide technical guidance while remaining hands-on.

Design, develop, test, and maintain production software for semiconductor test systems. Integrate AI and LLM capabilities into Java- and C++-based platforms, develop Python and Shell automation tools, define system interfaces, and evaluate integrations for performance, reliability, security, and maintainability. Collaborate with architects and AI specialists, apply engineering best practices, lead integration projects, and provide technical guidance in Linux environments.

Evaluate generative AI systems, RAG pipelines, and agents using Python-based frameworks and evaluation platforms. Responsibilities include model and agent quality assessment, Responsible AI validation, RAI metrics reporting, golden and synthetic dataset creation, and establishing AI observability and tracing with OpenTelemetry.

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.

Lead the architecture and development of scalable full-stack Sales AI applications. Integrate generative AI models, retrieval systems, APIs, and agentic workflows into secure production solutions. Guide architecture across front-end, back-end, data, integrations, and cloud infrastructure while improving testing, delivery, observability, reliability, and maintainability. Lead technical decisions, investigate complex issues, mentor engineers, and collaborate with product, AI, data, security, and engineering teams.

Leads enterprise technology strategy, delivery, and transformation across Finance, FinTech, Legal, and Risk. Oversees product platforms, Agile and DevOps adoption, AI and data initiatives, digital transformation, change management, vendor partnerships, governance, and technology operations. Partners with executive stakeholders to align global priorities, integrate platforms, manage dependencies, and deliver scalable, secure, value-driven outcomes across a federated enterprise technology organization.

Lead the design and production delivery of agentic AI systems, translating domain expertise into agent architectures, tools, evaluations, guardrails, and reusable engineering patterns. Direct technical strategy for an engineering pod, establish tracing and regression infrastructure, and communicate effectively with engineers, executives, clients, and domain experts.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Designs and governs enterprise AI, data, analytics, cloud, and platform architectures. The role develops AI reference architectures, data engineering standards, cloud infrastructure patterns, resilience controls, responsible AI frameworks, and technology roadmaps. It guides engineering teams, reviews strategic solutions, establishes governance, mentors technical staff, and partners with business, security, compliance, and senior leadership to enable scalable, secure, cost-effective AI and data platforms.

Build and deploy production AI solutions natively in Snowflake, including machine learning models, Cortex Agents, document intelligence, semantic data access, and search services. Develop evaluation frameworks, assess AI quality, work with messy business data, and communicate findings to stakeholders. Collaborate with platform engineers while owning problem framing, experimentation, solution development, governance, and production delivery.

Build and deploy production AI solutions natively in Snowflake, including machine learning models, Cortex Agents, document intelligence, semantic data access, and search services. Develop evaluation frameworks, assess model and LLM quality, work with messy business data, and communicate findings to stakeholders. The role covers the full AI project lifecycle from problem framing and prototyping through governance and production deployment.

Build and maintain system software and infrastructure for deploying, benchmarking, automating, and qualifying AI applications and models. Responsibilities include supporting inference backends, managing local model repositories, developing performance and correctness measurement systems, processing workload metrics, implementing diagnostics and observability, and maintaining automated build, integration, deployment, and qualification pipelines. The role also involves debugging system architectures, analyzing bottlenecks, and improving scalability across Linux and Windows environments.

As a Senior Business Systems Analyst, you will define system goals, capture business requirements, and work with engineering teams on SFDC implementations.

Conduct AI visibility audits across ChatGPT, Perplexity, and Google AI Mode; identify content, schema, and technical improvements; use AI tools to run scripts; prepare client reports and updates; and document repeatable processes. The role involves live client work, communication, experimentation, and learning SEO, analytics, and AI visibility tools on the job.

Leads quality assurance for AI training data by managing review queues, auditing annotations, monitoring quality trends, coaching reviewers, clarifying guidelines, training new team members, identifying recurring errors, and escalating complex issues. The role also supports corrective actions and continuous improvement while ensuring accurate, consistent, and timely project delivery.

Coordinate workforce planning and deployment for over 1,800 contributors across AI training projects. Allocate resources based on project demand and capacity, maintain deployment and availability records, monitor onboarding and throughput, identify conflicts and shortages, track requests, coordinate with project and training teams, conduct shift handoffs, and improve resource-planning processes and workflows.

Leads AI data annotation teams through project delivery, translating client guidelines into workflows, setting production targets, monitoring quality and productivity metrics, coaching team members, managing staffing and deadlines, and resolving operational risks. Partners with QA and tooling teams to improve processes and maintain accurate, timely training data delivery in a fast-paced environment.

Lead enterprise architecture strategy for a global financial institution. Review fraud-domain application portfolios, assess architecture options and build-versus-buy decisions, conduct rationalization assessments, define target-state “north star” architectures, and create estimates, roadmaps, and transition architecture diagrams. Serve as a technical authority while translating complex business challenges into scalable solutions and collaborating with technical and business stakeholders.

Leads emerging product innovation from opportunity discovery through prototyping, pilots, product-market validation, commercialization, and scale. Owns AI, data, analytics, automation, and platform product strategies; establishes experimentation and stage-gate governance; develops investment cases and go-to-market plans; and aligns cross-functional teams and executives. Manages product leaders, builds incubation practices, and drives measurable customer, revenue, efficiency, and strategic outcomes.

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.