Top Artificial Intelligence Jobs in Pune

The role involves developing full-stack web apps, creating APIs, improving lead capture models, and collaborating across teams to enhance the Social Planner product.

Drive technical excellence in AI/ML by leading software solutions, mentoring teams, collaborating with stakeholders, and shaping technology strategies.

Lead the design and implementation of AI-driven software solutions, mentor teams, contribute to strategic planning, and foster innovation in the organization.

Manage and design integration processes using Boomi and Workato, supporting and developing solutions for complex business issues. Mentor team members and ensure effective communication with stakeholders.

Define and deliver AI agents for enterprise spend management. Own vision, architecture, and roadmap; design agent patterns, observability, and closed-loop training; partner with engineering, data science, UX, and go-to-market teams; engage Fortune 100 stakeholders to ship reliable, explainable agentic workflows for invoicing, vendor onboarding, contracts, dispute resolution, and cross-border payments.

Lead and grow a Colombia-based AI engineering team (3-5 engineers), own delivery across multiple client engagements, set technical architecture and standards, stay hands-on for high-stakes builds (agents, multi-step pipelines, integrations), partner on engineering strategy, and represent the team with clients and internal stakeholders to ensure quality, timely delivery, and scalable production AI systems.

Design, build, and deliver enterprise-grade Generative AI and AI/ML platform capabilities: reusable services, APIs, microservices, RAG and agentic solutions, model lifecycle tooling, inference and monitoring, event-driven streaming with Kafka, CI/CD and DevOps automation, and platform observability to accelerate AI adoption across business domains.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.

Lead the intelligent transformation of operations support functions through AI and data solutions, overseeing hands-on technical delivery and team leadership in AIOps initiatives.

Design, execute, and maintain test plans for an AI-powered platform. Perform functional, integration, regression, end-to-end, API, browser/device compatibility, and performance testing. Validate AI features, prompts, and generated responses; track and verify bug fixes; collaborate with developers and product teams; prepare launch-readiness reports and recommend stability and UX improvements.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.

Design and build production AI systems for BJAK's AI Neobank: AI agents, LLM apps, RAG/retrieval, tool-calling, and automations for onboarding, KYC, risk, CRM and operations, with focus on accuracy, latency, cost, security, compliance and reliability.

The AI Engineer will develop and deploy AI/ML models and solutions, collaborate with teams, and ensure production readiness while addressing complex business problems.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

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 build production AI-powered analytics: conversational interfaces, LLM agents, semantic data models, and scalable data pipelines. Partner with Product and Engineering to deploy RAG/MCP-based solutions, integrate AI with enterprise data and cloud platforms, mentor teams, and promote Responsible AI practices to improve decision-making and automation across the company.

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.

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.