Top Artificial Intelligence Jobs in Pune

Design, build, and deploy GenAI/LLM-powered solutions (chatbots, summarization, document processing, knowledge assistants). Implement prompt engineering, data preprocessing, embeddings, RAG pipelines, and vector DB integrations. Integrate models via REST APIs, collaborate with product and engineering teams, monitor model performance, and ensure responsible AI practices and production-ready deployment.

Lead development and production deployment of AI/ML models focused on personalization, recommendations, segmentation, and predictive analytics. Collaborate with data engineering and development teams to ensure data readiness, integrate models into production (real-time systems like Kafka), apply AIOps to monitor performance, and mentor an AI/ML team.

Build and integrate agentic and generative AI solutions: implement RAG pipelines, integrate LLMs and agent frameworks, write production-quality Python, apply TDD, build data pipelines and visualizations, and collaborate in Agile consulting engagements to deliver client-facing AI products.

Lead design and delivery of agentic and generative AI systems including multi-agent orchestration, RAG pipelines, LLM integration, and AI-coding agent workflows. Provide technical leadership on client engagements, mentor engineers, perform EDA and statistical analysis with Python, and enforce engineering standards, TDD and scalable system design while contributing to practice development.

Lead enterprise customers in deploying Generative AI solutions, architect production systems, and mentor on AI Factory deployments while optimizing MLOps and inference processes.

Lead the design, development, deployment, and optimization of AI-driven applications, especially RAG-based chatbots and generative AI solutions. Build scalable systems with Python and Java, deploy them on AWS and Azure, and apply machine learning, deep learning, NLP, vector databases, embeddings, and LLM integrations. Collaborate with cross-functional teams, manage technical deliverables, mentor engineers, ensure quality through testing and optimization, and adopt emerging AI technologies.

Lead design and delivery of RAG-based chatbot and AI solutions: architect scalable Python/Java systems, deploy and manage on AWS/Azure, mentor engineers, ensure quality, and research emerging AI methods.

Lead strategy, roadmap, and execution for Maropost's Da Vinci AI platform. Prioritize AI opportunities across product clouds, drive assistive/agentic automation and personalization, establish responsible AI governance, partner with cross-functional teams from discovery to launch, measure business impact, and present outcomes to executive leadership.

Work across growth, product, user research, and market analysis using AI tools. Run end-to-end GTM experiments, own funnel metrics, test product releases, and fix urgent operational issues. Communicate findings clearly and move quickly to ship improvements.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Own the roadmap and delivery of AI automation products, including reliable AI agents, tool-calling workflows, n8n automation pipelines, API integrations, and React-based interfaces. Partner with sales, content, and marketing to identify opportunities, then monitor, debug, and improve shipped solutions. The role requires strong product ownership, hands-on AI and automation experience, technical range across frontend and integrations, and fluency with LLMs and agent frameworks.

Quota-carrying enterprise sales role focused on generating, qualifying, and closing AI opportunities across an assigned region. Partner with field teams and AI ecosystem partners, craft executive-quality pitch decks and proposals, manage CRM and forecasts, and translate AI technologies into business value. Drive both infrastructure/product sales and consulting engagements through the full sales cycle while embedding into account planning and co-selling motions.

Build and deploy full-stack AI applications integrating LLMs, RAG architectures, vector databases, and multi-agent systems. Develop Python backends, APIs, ingestion pipelines, responsive interfaces, and real-time dashboards. Design and optimize PostgreSQL databases, containerize applications with Docker and Kubernetes, manage AWS and GPU infrastructure, and operate Linux environments. Mentor developers, conduct demonstrations, evaluate emerging AI technologies, and improve reliability through testing, debugging, performance tuning, and quality processes.

Leads the architecture and delivery of production-grade agentic and generative AI systems, including LLM agents, RAG pipelines, multi-agent workflows, and scalable cloud applications. Guides technical architecture, production engineering, LLMOps, evaluation, observability, security, and responsible AI practices. Mentors AI/ML and backend engineers, conducts design and code reviews, and collaborates with product, data, platform, security, compliance, and executive stakeholders.

Designs and implements computer vision and machine learning algorithms for aquaculture applications. Responsibilities include developing prototypes, conducting experiments, building data-processing and training pipelines, maintaining model testing and statistical verification, extending AI codebases, documenting research, leading long-term research, recruiting talent, and tracking AI developments. The role may also involve production deployment, inference optimization, MLOps, cloud platforms, and occasional visits to aquaculture sites.

Drive process improvement, automation, and analytics across SOX, Internal Audit, and Enterprise Risk Management functions. Map workflows, eliminate manual effort, build CI/CD-enabled automation and testing frameworks, and develop Power BI dashboards using Python and SQL. Configure Jira workflows and compliance automation, implement compliance-as-code monitoring, and apply GenAI with audit-ready guardrails. Partner with technology and compliance teams to deliver scalable, measurable, and defensible solutions.

Design, develop, and optimize autonomous AI agents capable of reasoning, planning, tool use, and multi-step execution. Build agent frameworks, human-AI collaboration mechanisms, monitoring and evaluation systems, and production integrations. Apply large language models, prompt engineering, reinforcement learning, planning algorithms, workflow orchestration, and system design. Collaborate cross-functionally while researching advances in agent technology and improving reliability, accuracy, safety, and efficiency.

Support product operations by collecting and tracking user feedback, coordinating product issues across teams, organizing requirements, analyzing basic data, maintaining web pages, and assisting with AI-related projects. The role requires cross-functional communication, problem-solving, execution skills, bilingual English and Mandarin proficiency, and availability for 3–5 days per week for at least six months.

Design, develop and deliver scalable, secure software solutions. Collaborate with product, design, and engineering teams; lead or guide teams; enforce code quality through reviews and unit testing; analyze complex data; influence decisions; mitigate risk and strengthen controls; stay current with technology trends.

Design, develop, and deliver scalable, maintainable software solutions. Collaborate cross-functionally, participate in code reviews, apply secure coding and unit testing, lead or guide teams, influence policy and risk controls, and communicate complex technical information to stakeholders.

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

Design, develop, and deploy generative AI agents and end-to-end ML pipelines. Lead prompt engineering, LLM integration, model training, and productionization, collaborating with customers and stakeholders to deliver AI solutions.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

As a Senior Agentic AI Lead, you will manage the agentic AI domain, engage with customers, oversee the AI lifecycle, and ensure effective data governance and analytics across various teams.

Own strategy, roadmap, and execution for HighLevel VoiceAI. Deliver reliable inbound/outbound AI voice agents integrated with telephony, CRM, workflows, and analytics. Improve agent setup, call quality, compliance, APIs/webhooks, monetization, and developer extensibility. Partner with engineering, design, support, and GTM to drive adoption, instrumentation, and measurable business outcomes for SMBs and agencies.