Top Artificial Intelligence Jobs in Pune

Lead and manage AI engineering teams to design, build, and maintain scalable ML/GenAI infrastructure and deployment pipelines on AWS. Drive technical strategy, governance, CI/CD and IaC practices, mentor staff, collaborate with stakeholders, and ensure secure, high-quality delivery aligned to business objectives.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Lead the development of secure, scalable software and applied AI solutions at Barclays. Responsibilities include designing AI applications, collaborating with product and engineering teams, conducting code reviews, ensuring testing and secure coding, guiding technical direction, mentoring colleagues, managing risks and controls, and advising stakeholders. The role requires expertise in applied AI, AWS, software architecture, leadership, and stakeholder communication, with desirable experience in big data, cloud data platforms, and data governance. Based in Pune.

Leads the design, development, and delivery of scalable, secure software and applied AI solutions. Drives data technology innovation, software architecture, responsible AI adoption, technical leadership, mentoring, stakeholder collaboration, project execution, risk management, and continuous improvement. Works with AWS services, AI frameworks, retrieval-augmented generation, vector databases, cloud data platforms, and governance controls.

Design, develop, and improve secure, scalable software and Generative AI solutions. Lead engineering delivery for the SOLD Simplification initiative, coordinating requirements discovery, planning, roadmap execution, stakeholder management, and platform modernization. Build full-stack applications using Python or Java, integrate LLMs and enterprise data sources, and apply cloud-native engineering, DevSecOps, testing, observability, resilience, and production support practices. Guide technical direction, manage risk and controls, and collaborate with cross-functional teams.

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.

Implement and optimize AI-enabled Contract Lifecycle Management solutions for enterprise customers. Lead implementations from discovery to deployment, configure workflows/templates, support data migration, develop and optimize AI prompts for contract analysis, and provide technical consulting, training, and stakeholder collaboration to ensure successful adoption and continuous improvement.

Own the architecture and delivery of production-grade generative and agentic AI systems, including LLM agents, RAG pipelines, multi-agent workflows, APIs, and cloud-native services. Establish AI engineering, evaluation, observability, guardrail, and LLMOps practices while mentoring AI/ML and backend engineers. Collaborate with Product, Data, Platform, Security, and Compliance teams to deliver scalable, secure, and business-ready solutions, and provide technical direction on GenAI strategy and architecture.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Leads customer-facing design and implementation of cloud, platform, infrastructure, data, and AI solutions. Responsibilities include cloud migration and modernization, Infrastructure as Code, CI/CD, Kubernetes, automation, GitOps, platform security, observability, and AI-assisted development. The consultant conducts workshops, creates reusable platform capabilities, enables responsible AI adoption, mentors engineers, supports proposals and pre-sales, and ensures secure, reliable, governed, and cost-effective production solutions.

Lead design, development, and deployment of production-grade LLM and RAG-based AI solutions. Improve retrieval performance, build automated evaluation and validation frameworks, instrument pgvector retrieval KPIs, integrate metrics into CI/CD, and mentor mid-level engineers while driving technical reviews and best practices.

Develop and maintain AI-assisted development capabilities for the Eclipse-based SmarTest semiconductor testing environment. Integrate and optimize GitHub Copilot, resolve Eclipse and integration defects, implement customer-specific features, and perform testing and quality assurance. Translate customer requirements into technical solutions, provide technical assistance, evaluate changes in AI and Eclipse technologies, and collaborate with Microsoft, Advantest teams, and external partners to improve developer productivity and user experience.

Develop and optimize deep learning, graph, and ML techniques for GPU architectures; work with customers to deliver AI solutions; collaborate with architecture, research, libraries, and system software teams.

Lead architecture and delivery of enterprise AI platforms and products. Define AI standards, drive GenAI adoption, build scalable MLOps and observability, mentor engineers, and guide technical strategy.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Design and deliver intelligent platforms, automation solutions, and AI-driven capabilities. Analyze complex production systems, identify failure modes, and improve reliability, efficiency, productivity, and mean time to recovery through AI, automation, operational intelligence, and modern engineering practices. Build scalable cloud-native solutions using Kubernetes, APIs, MLOps, generative AI, and workflow orchestration while mentoring teammates and driving continuous improvement.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Build and operate backend services and infrastructure for LLM- and voice-driven healthcare applications. Implement APIs, productionize LLM workflows, deploy cloud containerized services, and monitor performance and reliability end-to-end.

Evaluate and annotate physics content, create problem sets and technical documentation, and assess AI-generated responses for scientific accuracy. Contribute to dataset creation and provide expert feedback to improve AI model training. Collaborate remotely with project teams to ensure timely, high-quality deliverables and improve evaluation methodologies.

Contractor engineering SME to review and create technical engineering content for AI training: analyze documentation, develop examples and datasets, evaluate AI outputs for technical accuracy, and collaborate with teams to improve model performance and processes.

Contractor role to create original, high-quality financial documents (reports, analyses, models) with rich visuals (tables, charts, graphs) to train AI. Ensure accuracy, anonymize sensitive data, contribute long-form documents (10–100 pages), and clarify submissions as needed.

Remote contractor role to create and evaluate mathematical problems, proofs, and datasets to train and improve AI models. Assess AI-generated solutions for correctness, produce technical documentation, and collaborate with cross-functional teams to develop benchmark datasets and provide actionable feedback to improve model reasoning.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Implement and customize ServiceNow ITSM and Service Portal solutions, perform integrations (REST, SOAP, MID Server), maintain Discovery and CMDB, develop and optimize server/client scripts (Business Rules, Script Includes, UI Policies), run workshops/demos, translate client requirements into user stories, mentor junior developers, and participate in Agile delivery cycles.