Top Artificial Intelligence Jobs in Pune

Lead design, build, and production deployment of agentic AI systems on GCP. Architect scalable microservices, implement LangChain/LangGraph agents, establish MLOps and observability (Langfuse), mentor engineering teams, define SLAs/SLOs, and collaborate with product and SRE to deliver reliable, production-grade AI solutions.

Design and own architecture for production-grade Generative AI applications. Lead system design across backend, frontend (React), and AI infrastructure for LLM inference, RAG pipelines, and agent workflows. Define technical strategy, standards for prompt design, model integration, observability, and enterprise readiness (security, privacy, governance). Mentor teams, run POCs, review designs and code, and partner with product and business stakeholders to translate requirements into scalable AI solutions.

Lead development of production-grade generative AI and multimodal systems: design agentic pipelines, fine-tune LLMs, build retrieval and KG-augmented solutions, deploy scalable model APIs on cloud, and mentor engineers while partnering with product and CX teams.

Lead development of production-grade generative AI systems: design and deploy LLM-based, multimodal, and agentic applications; build retrieval and KG-augmented pipelines; implement fine-tuning and alignment (LoRA/QLoRA, RLHF/RLAIF); deploy scalable APIs and MLOps; collaborate cross-functionally and mentor junior engineers.

Lead GenAI engineer role to design, build, and deploy production-grade LLM-based, multimodal, and agentic systems. Responsibilities include model fine-tuning, retrieval and KG-augmented systems, API/microservice deployment, cloud/MLOps integration, monitoring, mentoring, and cross-functional collaboration to deliver scalable, safe, and cost-optimized AI solutions.

Lead design and build of generative AI, multimodal, and agentic systems. Develop and fine-tune transformer models, retrieval/knowledge-graph augmented systems, and production APIs. Deploy and monitor ML services in cloud, collaborate cross-functionally, mentor engineers, and ensure safety, evaluation, and scalable MLOps practices.

Lead architecture and delivery of production-grade Generative AI applications. Define technical strategy, design scalable LLM inference and RAG platforms, frontend AI UX, and reusable GenAI components. Ensure security, privacy, governance, and enterprise readiness while partnering with product and engineering teams and conducting POCs and technical reviews.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Build and deploy production-ready GenAI systems (LLM, RAG, agentic agents) and document intelligence pipelines. Work with LangChain/LangGraph/LlamaCloud, embeddings and vector DBs, deploy on AWS/GCP/Azure, and apply ML/MLOps and model evaluation. Deliver fast, independent end-to-end solutions as a freelance/consultant.

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Early-career intern will support AI projects for compliance and risk (KYC, KYB, EDD). Tasks include testing and optimizing LLM/Agent workflows, reviewing prompts and model outputs, documenting findings and edge cases, supporting small-scale implementations, and performing operational analysis to improve processes.

Lead product strategy for HighLevel Knowledge Bases and Ask AI, defining architecture, ingestion, retrieval, evaluation, permissions, observability, and commercial models. Drive cross-team platform standards, onboarding, testing datasets, and integrations so AI assistants reliably retrieve context, invoke tools/agents, execute actions, and surface auditable, safe results for multi-tenant SMB customers.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Build and maintain full-stack web applications using React/Vue frontends and Java/Spring Cloud microservices. Design and consume REST/AI model APIs, work with SQL and NoSQL databases, use AI-assisted development tools, own features end-to-end, collaborate across product and design, ensure high-quality UI/UX, write tests, and contribute to documentation and architecture.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and execute organic marketing strategies to drive lead generation. Manage SEO, content-driven email campaigns, and webinars. Analyze campaign performance and optimize for improvements. Support revenue operations (RevOps) to align marketing with sales.

Design, develop, and deploy advanced AI/ML models; manage large-scale data pipelines; build conversational and generative AI solutions; collaborate with stakeholders; monitor and optimize production models; document workflows and implement industry best practices.