Top Artificial Intelligence Jobs in Pune

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.

The Cloud Platform Engineer builds and maintains infrastructure platforms, ensuring reliability and security, handles incident management, and develops automation solutions. They collaborate with teams to align IT infrastructure with business objectives while mentoring team members and engaging in complex data analysis.

Lead and grow the India Data & AI organization (~150 people), define offerings and strategy, collaborate with Nordic clients to design, build, and operate cloud data platforms, data pipelines, analytics, GenAI and agentic AI solutions, and develop a high-performing team.

Lead development of production-ready AI/ML systems focusing on computer vision and image processing. Apply generative AI and deep learning, mentor teams, drive cross-team technical decisions, and deploy solutions on cloud or on-prem pipelines.

Build, test, and deploy production-ready ML services and data workflows on GCP. Implement clean, tested code, CI/CD, and monitoring; create evaluation pipelines and metrics; troubleshoot reliability and latency; and collaborate with product, operations, and engineering stakeholders to deliver end-to-end ML features.

Lead design, build and maintenance of data architectures, pipelines, warehouses and lakes; enable ML lifecycle by preparing governed datasets, collaborating with data scientists, and driving data governance, lineage, access controls and analytics-ready schema design.

Build, test, deploy, and support AI-powered applications, workflows, automations, and integrations using Python, APIs, enterprise data, and LLM technologies. Collaborate with data science, engineering, product, platform, and business teams. Support model training, evaluation, monitoring, retraining, and continuous improvement, while addressing performance, reliability, latency, cost, and data-quality challenges.

Designs, builds, and optimizes enterprise-scale software systems and cloud platforms. Develops infrastructure as code with Terraform, automates infrastructure using scripting, and implements CI/CD pipelines. Applies DevSecOps and SRE practices, including monitoring, incident management, and performance tuning. Integrates generative AI into engineering workflows and leads architectural and technical decisions across AWS, Azure, and GCP environments while collaborating with stakeholders and technical teams.

Designs, develops, and maintains automated test frameworks using C#, JavaScript, Playwright, Selenium, and related tools. Integrates automated testing into CI/CD pipelines, performs API and ETL testing, validates data with SQL, investigates failures, documents defects, and supports exploratory and negative testing. The role collaborates with development and cross-functional teams, contributes to test strategy and framework design, tracks coverage metrics, and mentors junior automation engineers in an Agile product development environment.

Lead and grow a team building an on-device AI inference platform for RTX/ DGX GPUs. Drive cross-functional alignment, architect inference runtimes, optimize models and pipelines for low-latency, memory-efficient local deployment, and establish processes for system-level debugging, performance/accuracy trade-offs, and production readiness. Mentor engineers and deliver roadmap-aligned, high-quality releases.

Lead enterprise AI architecture, governance, standards and platform selection. Define AI/agent patterns (A2A, MCP), integration and security controls, govern Architecture Review Board decisions, produce reference architectures and playbooks, and enable global adoption across cloud, data, ERP/CRM and regulated manufacturing/MedTech environments.

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.

Leads the design, development, and optimization of secure enterprise software systems and cloud platforms. Oversees DevSecOps practices, infrastructure automation, CI/CD, IaC, cloud architecture, containerized and serverless deployments, monitoring, compliance, and SRE operations. Translates business needs into technical roadmaps, mentors engineering teams, and integrates generative AI into software and automation practices.

Design and optimize on-device AI inference stacks for RTX and DGX systems. Build inference runtimes, optimize models and pipelines (quantization, pruning), perform system-level debugging and performance-accuracy tradeoffs, and collaborate across software, research, architecture, and product teams to ensure production readiness.

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.

Lead the development of a core AI-powered platform component at Autodesk, overseeing design, implementation, and collaboration with global teams. Responsible for full engineering cycle and mentoring junior members.

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.

Hands-on Technical Lead for computer vision projects: design, develop, optimize and deploy CV algorithms and pipelines on NVIDIA GPUs and edge devices. Lead and mentor a team, conduct code reviews, work with customers on requirements, and drive production performance and deployment of AI models.

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.

Lead infrastructure quality engineering and automation for hybrid enterprise platforms. Architect IaC validation, CI/CD, microservices (Java/Spring Boot/MS SQL), cloud transformation (AWS/Azure), observability, and AI-driven automation. Mentor 20+ engineers, define KPIs, drive reliability, testing strategy, and cross-team collaboration for scalable, production-ready infrastructure.

Lead enterprise AI and software delivery using Agile/Scrum. Manage sprint planning, backlog, velocity and release metrics; create project plans and roadmaps; identify and mitigate risks; coordinate cross-functional teams; drive stakeholder and executive reporting; support AI modernization and cloud modernization initiatives to ensure predictable delivery.

Internship role to design and build LLM-powered recommendation and agentic AI systems for financial and Web3 scenarios. Tasks include candidate generation, ranking, tool routing, post-training (SFT/RL), datasets and evaluation pipelines, prototyping agent workflows, and integrating prototypes into production with cross-functional teams.