Top Artificial Intelligence Jobs in Pune

Lead design, training, and fine-tuning of large-scale ML/DL models (LLMs and generative systems) for drug discovery. Build scalable training pipelines, data pipelines, and production-ready model deployments; collaborate with engineering teams; translate research into applied AI solutions and continuously improve model performance and reproducibility.

Create original graduate-to-PhD-level problems and rigorous step-by-step solutions in STEM fields, review AI-generated answers to identify reasoning or factual errors, and ensure clarity and verifiability. Work remotely and asynchronously through an online platform after identity verification and an initial assessment.

Execute fashion design briefs using Raspberry AI to produce flats, tech packs, colorways, prints, mood boards, and concept visuals. Translate creative direction into precise prompts, iterate on feedback, maintain brand-consistent quality, document workflows, flag platform issues, and partner with US Customer Success for intake and delivery under tight turnarounds.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and build production LLM-based agent systems, governance knowledge pipelines, hybrid retrieval and evaluation frameworks, and platform infrastructure. Implement behavioral shaping, observability, and constraints to ensure agents align with governance and compliance requirements, collaborating with product and data teams.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Provide hands-on technical support to customers deploying, operating, and scaling ArangoDB-based AI systems. Diagnose issues, optimize queries and performance, configure deployments across Kubernetes and cloud/on-prem, debug networking/storage, script automations, and document solutions while collaborating with Product and Engineering.

Design and presales of end-to-end AI/HPC infrastructure: size GPU clusters, storage, networking, Kubernetes/container platforms, integrate liquid-cooled racks with facility constraints, validate solutions with OEMs, and support delivery for scalable, operable AI deployments.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Own post-sale success for enterprise customers: drive adoption, retention, and expansion of Cursor. Lead end-to-end rollouts, engage engineering leaders, convert use cases to repeatable workflows, monitor usage, and relay product feedback to influence roadmap.

Design and build agentic AI solutions and complex agent workflows using Python. Train and deploy models on AWS or other cloud, build prototypes with Streamlit/Gradio, apply MLOps, collaborate in Agile/Scrum with Git and JIRA.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Build and maintain Python/FastAPI backend services and React frontends, design and consume REST APIs, work with relational databases, and implement/debug AI-enabled features including vector DBs, embeddings, semantic search, and RAG workflows. Improve existing codebases, collaborate with product and design, participate in testing, deployment, and monitoring, and ensure performance, security, and maintainability.

The IT Operations Engineer will manage AI and Data platforms, ensure operational health, lead incident resolution, and automate tasks while collaborating with engineering teams.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

Lead architecture and engineering of enterprise-scale Generative AI and data platforms. Design reusable platform services for end-to-end AI/ML lifecycle, build agentic applications and event-driven systems, enforce CI/CD and DevSecOps, ensure security/governance/observability, mentor engineers, and drive platform modernization using cloud-native, containerized, and distributed technologies.

Lead platform automation for enterprise Generative AI and data platforms: design and build IaC, CI/CD, observability, deployment automation, and self-service capabilities to accelerate AI/ML lifecycle, reliability, governance, and developer productivity across cloud-native distributed environments.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Build and maintain lab environments and CI/CD pipelines for the Skylar AI platform. Create automated end-to-end tests, simulate user journeys and telemetry, validate integrations and performance, manage versioning and rollback tests, support data generation, and document environments. Partner with product, data science, QA, and engineering to ensure reliable monthly updates and platform availability.

The ServiceNow Solution Architect - AI role involves leading and mentoring a team, designing and deploying AI-driven applications, managing application development, and integrating solutions into ServiceNow modules. Responsibilities include coordinating with stakeholders, troubleshooting, and ensuring adherence to technical standards.

Design, build, test, and ship production AI-enabled software and reusable components for enterprise workflows. Implement context-aware agentic systems, integrate models and APIs, write unit tests, debug production issues, lead code reviews, mentor engineers, participate in on-call rotation, and collaborate with U.S. business partners while working remotely from India.

Design, build and deploy AI workflows using Azure AI, Copilot Studio and Microsoft Fabric; integrate solutions with SAP and Salesforce; own end-to-end delivery, DevOps/MLOps/AIOps practices, governance, monitoring, and stakeholder adoption; provide technical leadership and reusable patterns to scale AI adoption across the business.

Design and implement end-to-end application layers and APIs that integrate AI agent frameworks with relational databases. Build multi-cloud deployable services, optimize performance and queries, ensure multi-tenant data isolation and security, integrate vector/RAG systems and monitoring, and own production features from design through deployment and observability.

Design and implement end-to-end AI platform features integrating agent frameworks, vector search/RAG, and relational database capabilities. Build multi-cloud deployable services, enforce multi-tenant data governance, optimize performance, and own APIs, monitoring, and security.

Design, develop, and deploy ML models and LLM-based applications; implement RAG and vector database pipelines; fine-tune models, optimize inference and scalability; build REST APIs and backend integrations; monitor AI system performance; collaborate cross-functionally and stay current with AI advancements.