Top Artificial Intelligence Jobs in Pune

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Designs and maintains a centralized AI platform and agentic framework for secure enterprise deployment and access. Builds scalable AI agents, unified APIs, distributed systems, and microservices; integrates agents into customer and internal applications; optimizes cloud-native workloads; ensures security and regulatory compliance; and leads technical strategy, mentoring, performance analysis, and capacity planning.

Designs and maintains a centralized AI platform and agentic framework supporting secure deployment, management, and integration of AI agents. Responsibilities include building scalable agentic solutions, developing unified APIs, integrating agents into applications, optimizing cloud-native workloads, ensuring security and compliance, conducting capacity planning, mentoring engineers, and shaping the platform roadmap.

Lead AI Engineer building production-grade backend microservices, API integrations, cloud-native services, and AI-native features. Responsibilities include GenAI and agentic workflow integration, code reviews, testing, troubleshooting, cloud deployment, intelligent automation, client technical consulting, solution design, pre-sales support, mentoring, and AI engineering framework development.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

Develop, deploy, and maintain machine learning and AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Build scalable data and ETL pipelines using cloud platforms, big data technologies, and databases. Apply TensorFlow, PyTorch, Hugging Face, and cloud AI services while creating data visualizations and optimizing analytics workflows.

Audit AI agent call and chat transcripts against quality and compliance standards, identify regressions and edge cases, document actionable engineering issues, and participate in client QA and audit calls. Reconcile internal findings with client audits, explain performance data, and provide recurring quality insights to the Technical Program or Product Manager for release communications and client reviews.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Lead enterprise AI and machine learning strategy, architecture, development, and production deployment. Design predictive analytics, generative AI, LLM, RAG, and agent solutions; establish MLOps, governance, monitoring, and responsible AI practices; align initiatives with business value and ROI; communicate with executives and stakeholders; prioritize and deliver AI programs; and mentor data scientists, ML engineers, and analysts.

Build and deliver production-grade AI-native and cloud-native solutions, including backend microservices, API integrations, GenAI features, agentic workflows, and intelligent automation. Responsibilities include testing, code reviews, troubleshooting, technical debt reduction, cloud deployment, client technical consulting, proof-of-concept support, mentoring junior engineers, and contributing to engineering frameworks and architecture discussions.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Design, build, and scale production AI agents across EveryWatch. Own the agent roadmap, architecture, shared tools, memory, state, observability, evaluation, and cost controls. Harden WatchChat, develop RAG and tool-calling systems, establish measurable quality standards, and ship solutions directly in Python. Collaborate with sales, product, data, and engineering teams to identify high-impact use cases and take agent concepts from discovery through production.

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

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Design, build, test, deploy, and monitor production automations that integrate Python, REST APIs, cloud data stores, and Large Language Models. Implement prompt engineering, structured output parsing, validation, and orchestration; create lightweight React/FastAPI interfaces when needed; follow agile practices, maintain shared libraries and repos, and hand off documented automations to operations teams.

Develops end-to-end cloud applications using C#/.NET, ASP.NET Core, Next.js, and TypeScript on Azure. Builds secure APIs, enterprise integrations, containerized deployments, tests, CI/CD workflows, and production support processes. Integrates Azure OpenAI and AI services to create agents, tool-calling workflows, MCP integrations, and RAG solutions with search, embeddings, grounding, and citations. Collaborates with architects, developers, QA, and program teams while evaluating AI quality, reliability, latency, and cost.

Lead AI-generated content production by editing, fact-checking, and aligning output with client-approved data. Translate SEO strategies into content briefs, maintain consistent brand voice across blogs, landing pages, and social copy, and create client-specific source-of-truth documents to prevent factual errors. Requires a portfolio demonstrating original and AI-assisted writing, SEO content knowledge, and strong fact-checking skills. Education or admissions content experience is preferred.

Lead the end-to-end product experience across acquisition, onboarding, avatar selection, chat, media generation, and retention. Drive conversion, retention, and LTV through hypotheses, prioritization, A/B testing, and data-informed design. Establish the product’s design direction and design system, lead a multidisciplinary design team, and partner with product, analytics, marketing, engineering, research, and support.

Build and operate Rubiscape’s AI platform infrastructure, including LLM gateways, RAG pipelines, prompt management, vector databases, inference APIs, guardrails, model serving, and observability. Integrate open-source and hosted models across SaaS, BYOC, and air-gapped deployments while supporting multi-tenant security, audit logging, usage metering, and regulated-sector requirements.

Build and operate Rubiscape’s internal AI-assisted developer platform, including custom LLM agents, IDE integrations, CLI tools, spec-to-code pipelines, and CI/CD quality gates. Manage prompt versioning, agent configurations, LLM logging, and automated code review and test generation. Integrate external AI development tools, monitor toolchain performance, collaborate with engineering teams, and document architecture and operational procedures.

Own the vision and multi-quarter roadmap for Rubiscape’s AutoML, MLOps, and agentic AI products. Translate enterprise AI needs into measurable features, evaluate emerging AI techniques, conduct discovery with technical and business users, and lead responsible AI governance capabilities. Partner with research, marketing, and pre-sales on experimentation, go-to-market, positioning, demos, competitive differentiation, and model/API versioning for regulated enterprise deployments.

Design and ship production software across Rubiscape’s Decision Intelligence Platform using AI-assisted, spec-driven development. Responsibilities include translating functional specifications into implementation tasks, pairing with LLM coding tools, validating AI-generated code and tests, participating in AI-assisted reviews, creating prompt and agent patterns, monitoring production code quality, and collaborating with prompt and specification engineering teams.

Validates data movement, mappings, transformations, APIs, migrations, AI-agent workflows, business rules, guardrails, and user-interface outputs. Develops integration, regression, edge-case, and AI-output tests; verifies data integrity and schema conformance; evaluates agent decisions, tool usage, fallbacks, hallucinations, and human-review handoffs. Investigates defect sources across systems and communicates quality risks and findings to product, engineering, data, and business stakeholders.

Develop and deploy generative AI applications, integrate LLM APIs, perform text analysis using traditional machine learning and GenAI, and manage the machine learning lifecycle from problem definition through deployment and monitoring. Collaborate with cross-functional teams, translate business needs into technical solutions, evaluate new GenAI techniques, and document technical concepts.