Top Artificial Intelligence Jobs in Pune

Manage design and deployment of integration solutions using Boomi and Workato, ensuring successful implementation and troubleshooting of integration processes.

Lead automation and analytics for internal audit: design Power BI dashboards, data analytics, and AI/RPA pilots to enhance SOX testing, control validation, anomaly detection, and continuous monitoring. Liaise with IT, business, and external auditors; coach team members; support remediation and audit transformation.

Lead product quality across the SDLC by reducing defects, driving root-cause analysis, defining automation strategy and quality gates, building automated test frameworks (Playwright, API, integration, performance), integrating tests into CI/CD, enforcing release readiness, and using Claude Code and other AI tools to analyze issues and improve engineering outcomes.

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

Early-career full stack internship developing frontend and backend features under senior guidance. Integrate AI coding tools into workflows, translate product requirements to implementations, contribute across the product lifecycle, and suggest cross-team process improvements while learning full-stack practices.

Implement and configure AI-enabled ServiceNow solutions, modernize portals and workspaces using Next Experience and UI Builder, build workflows with Flow Designer, implement Now Assist/AI Search/Virtual Agent, develop integrations via Integration Hub and REST APIs, and collaborate with stakeholders to deliver scalable ServiceNow solutions.

Design and lead enterprise-scale Agentic and Generative AI architectures, multi-agent systems, and knowledge-driven retrieval solutions. Define governance, observability, LLM selection, and production best practices. Lead PoCs to production, mentor teams, and advise stakeholders on AI strategy and transformation.

Build and optimize on-device AI inference stacks for RTX and DGX systems. Design inference runtimes and execution stacks, optimize models and pipelines (quantization, pruning, sparsity), perform system-level debugging and performance-accuracy tradeoffs, and create infrastructure for performance sweeps and production readiness across various ML workloads and hardware.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.

Lead enterprise clients to improve AI search visibility through analysis, custom GEO playbooks, C-suite presentations, prompt testing, monthly reporting, and cross-functional product collaboration to drive platform adoption and measurable visibility gains.

Design polished, user-centric mobile and web interfaces for BJAK's AI-driven neobank products. Convert requirements into wireframes, flows, prototypes and high-fidelity UI. Maintain and evolve the design system, ensure accessibility and responsiveness, iterate using research and analytics, and collaborate with PMs and engineers to deliver pixel-perfect implementations across insurance, payments, investments and related financial services.

Lead design and architecture of Generative AI solutions on Azure and OpenAI, build end-to-end data pipelines and cloud infrastructure, produce solution blueprints, collaborate with stakeholders, and implement data preprocessing, API deployment, monitoring, and analytics delivery.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, develop, fine-tune, and deploy generative AI models (LLMs, diffusion, VAEs). Build data pipelines, APIs, and integrate AI into production while improving performance and collaborating with cross-functional teams.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Lead AI strategy and implement GenAI, agentic AI, and automation to modernize legacy systems, integrate AI into engineering lifecycle, and build production-grade, scalable AI solutions with cloud, containerization, MLOps, and data pipeline best practices.

Design and architect AI/GenAI solutions on GCP; create architecture diagrams; develop, deploy, and optimize ML models; preprocess and manage large datasets; provide technical guidance to engineering teams; and present solutions to clients.

Design, fine-tune, and deploy generative AI models (LLMs, diffusion, VAEs). Build data pipelines and APIs, integrate AI into production, optimize model performance and latency, and collaborate with cross-functional teams to deliver AI-powered solutions.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Lead planning, design, delivery and promotion of a customer-service chatbot management platform. Use AI and big data to improve CS operations (corpus training, SOPs, assistant replies, summaries). Collaborate with engineering, UX, and CS teams. Monitor KPIs and user cases to drive product improvements.