Top Artificial Intelligence Jobs in Pune

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Configure and customize Oracle Fusion Product Development and Product Information Management modules. Develop Redwood-compliant user interfaces and extensions using Oracle Visual Builder, build REST and SOAP integrations, and create optimized OTBI reports and dashboards. Support security roles, testing, user adoption, and transition to Redwood UX while applying expertise in item masters, BOMs, change management, quality management, SQL, and Fusion data structures.

The Lead AI Scientist will design and optimize LLM architectures, fine-tune models, develop AI systems, mentor junior members, and collaborate with clients and teams on AI solutions.

Own the end-to-end product experience for agentic AI products, partnering closely with the CPO, design, and engineering. Responsibilities include defining the roadmap, features, UX improvements, onboarding, in-app education, and product marketing content. The role requires deep product exploration, data-driven prioritization, identifying friction and gaps, creating comprehensive PRDs and prototypes, and shipping simple, high-impact improvements in a fast-moving startup environment.

Own the quality strategy and end-to-end testing pipeline for an AI-generated product handling real-money transactions. Build automated UI, browser, mobile, pre-merge, nightly, release, and production testing gates; integrate testing with CI/CD; evaluate external tools; and improve reliability of critical financial flows. The role partners with engineering and DevOps, uses AI coding agents daily, and drives quality initiatives as an autonomous individual contributor.

Leads and mentors a software engineering team building scalable, reliable, secure platform services and database connectors for enterprise data pipelines. Responsibilities include architecture reviews, distributed backend systems, operational excellence, observability, quality, security, KPI tracking, cross-functional collaboration, hiring, and end-to-end product ownership. The role requires strong technical leadership, system design expertise, scalability and performance optimization, and experience managing or leading engineering teams.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows supporting analytics, business intelligence, and AI applications. Build solutions with Databricks and cloud technologies, integrate structured and unstructured data, implement quality and governance practices, optimize architectures, and support production deployment and monitoring. Collaborate with cross-functional teams to translate business requirements into reliable technical solutions and troubleshoot complex data challenges.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Lead engineering teams and drive delivery of an AI platform simplification initiative at Barclays. Responsibilities include cloud platform engineering, AI and MLOps operationalization, API development, DevSecOps, platform operations, roadmap planning, stakeholder management, governance, risk controls, and mentoring. The role supports scalable AI services, LLM lifecycle management, observability, secure engineering practices, and strategic technology improvements from the Pune office.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Build and integrate AI agents, APIs, webhooks, databases, and business-system automations. Translate business processes and customer flows into simple, efficient workflows with minimal code. This is a remote, part-time individual-contributor consulting engagement requiring hands-on system building and immediate availability.

Oversee portfolios of Generative AI data operations projects, managing delivery, capacity planning, vendors, budgets, quality, risks, automation, and client reporting. Coach project managers and coordinators, collaborate with recruiting and finance, enforce data security and compliance, and improve processes across distributed teams.

Designs and develops scalable cloud-based and serverless software across the company’s platform. Builds prototypes and production services, applies AI throughout the SDLC, promotes modern Agile and Well-Architected practices, troubleshoots customer and internal issues, develops comprehensive test suites, and coaches software engineers. The role requires extensive distributed application experience and backend expertise across multiple programming languages, databases, cloud technologies, integrations, and workflow orchestration.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

Evaluate datasets and AI-generated outputs for accuracy, relevance, consistency, and quality. Provide written feedback to improve AI system performance, follow project guidelines, support quality assurance, and communicate findings clearly. The role requires strong critical thinking, analytical reasoning, attention to detail, organization, and independent work in a remote environment. Prior AI experience is not required; data annotation, content evaluation, quality assurance, research, and AI project experience are preferred.

Join Welo Data’s Hungarian contributor network for flexible, remote AI data projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create and test prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Leads enterprise Microsoft 365 transformation initiatives focused on SharePoint Online, Power Platform, Copilot, AI agents, automation, security, compliance, and governance. Architects and implements collaboration solutions, migrates legacy platforms, configures Purview and related controls, and develops AI governance strategies. Provides technical leadership, client engagement, workshops, training, and ongoing support while coordinating project risks and dependencies. Requires availability during Eastern Time business hours and deep Microsoft 365, security, programming, database, and cross-tenant migration expertise.

Build and own end-to-end evaluation frameworks for LLMs and multi-step AI agents, including benchmarks, automated and human-in-the-loop evaluators, golden datasets, regression suites, production monitoring, and quality dashboards. Partner with research, product, Trust & Safety, and legal teams to define rigorous metrics and translate evaluation results into training and deployment decisions. Mentor engineers and establish evaluation best practices.

Manages the organization’s AI governance program, including use case intake, approval workflows, governance committee coordination, meeting documentation, inventory maintenance, compliance reporting, audit trails, and regulatory process updates. Partners with AI leadership, senior stakeholders, and cross-functional committees to ensure AI initiatives meet organizational policies, responsible AI principles, emerging regulations, and risk management requirements.

Build and maintain AI-native full-stack applications across frontend and backend systems. Develop modular React interfaces, REST APIs using Python or JavaScript/TypeScript frameworks, and work with SQL or NoSQL persistence. Set up application stacks on cloud providers, contribute throughout the product lifecycle, apply sound software design principles, and continuously learn and share knowledge.

Develop AI applications using Python, generative AI, LLMs, RAG, vector databases, APIs, and agentic AI frameworks. Build and integrate solutions with models including GPT, Claude, Gemini, and Llama. Use LangChain, LangGraph, Docker, cloud platforms, and modern deployment tools. The role requires strong problem-solving, API development, prompt engineering, and immediate or short-notice availability for office-based work.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

Own enterprise pipeline generation for strategic accounts across BFSI, manufacturing, and government. Design account-based outbound campaigns, conduct consultative discovery with C-level buyers, maintain CRM data quality, track conversion metrics, and partner with Account Executives on account planning. Mentor junior BDRs, support playbook development, and engage prospects around Rubiscape’s unified data, BI, ML, and AI platform.