Top Artificial Intelligence Jobs in Pune

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Lead pre-sales and technical delivery for Microsoft Industry Solutions, mapping customer needs to Microsoft Cloud and AI solutions. Drive project planning, risk mitigation, stakeholder engagement, IP reuse, and business development while mentoring teams and ensuring operational compliance.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Designs, develops, tests, and supports Java-based cloud-native applications on Google Cloud Platform. Builds REST APIs, integrates AI services, applies secure coding practices, contributes to CI/CD and deployment automation, and implements observability through logging, metrics, dashboards, and alerts. Troubleshoots production issues, performs root-cause analysis, participates in architecture discussions and code reviews, collaborates across engineering and business teams, and mentors junior developers.

Designs, develops, and operates Java-based cloud-native applications on GCP. Builds RESTful APIs, AI-enabled capabilities, secure services, CI/CD pipelines, infrastructure-as-code, and observability practices. Troubleshoots production issues, performs root-cause analysis, supports compliance and secure engineering standards, and collaborates with DevOps, QA, product management, and security teams. Participates in architecture reviews, code reviews, technical documentation, and shared engineering standards.

Lead a hands-on AI-first engineering pod building production software for a regulated payments platform. Design agentic orchestration pipelines integrated with Slack, Jira or Linear, Git, pull requests, and CI/CD. Ship product prototypes, improve .NET systems, establish secure engineering guardrails, and promote responsible AI-assisted development. Lead modernization of a legacy .NET Core 2.1 estate, mentor distributed engineers, grow the pod into scrum teams, and influence technical and business stakeholders with minimal direction.

Design, build, deploy, and maintain AI-native web applications and internal platforms. Use LLMs, autonomous agents, and AI tools to accelerate development. Build secure APIs, databases, CI/CD pipelines, and containerized cloud deployments. Collaborate with stakeholders, conduct code reviews, implement guardrails, and ensure production reliability and security.

The AI Engineer will develop and deploy advanced AI solutions focusing on generative capabilities, build context-aware AI agents, collaborate on data management, optimize AI model performance, and stay updated with AI advancements.

Own and deliver production-grade AI assistant capabilities for Sophos Firewall, including agent workflows, tool integrations, guardrails, evaluation frameworks, and automation. Design scalable backend systems, APIs, and distributed services with strong reliability, performance, security, and operational excellence. Build natural-language firewall management and intelligent security automation while collaborating with engineering and security teams. Deploy and optimize cloud-based solutions on AWS, including infrastructure, networking, identity, observability, and safe rollout practices.

The AI Engineer will develop and deploy GenAI applications, serve as a technical advisor, and collaborate on strategic initiatives in AI at Databricks.

Design and deliver intelligent platforms, automation solutions, and AI-driven capabilities. Analyze complex production systems, identify failure modes, and improve reliability, efficiency, productivity, and mean time to recovery through AI, automation, operational intelligence, and modern engineering practices. Build scalable cloud-native solutions using Kubernetes, APIs, MLOps, generative AI, and workflow orchestration while mentoring teammates and driving continuous improvement.

Build and ship AI-powered features for fashion, beauty, and home using large-scale product, image, and consumer data. Prototype with LLMs, agents, multimodal and CV models, run experiments, measure impact, and take models end-to-end from hypothesis to production while collaborating with product, design, and business teams.

Design, develop, and maintain conversational AI chatbots and voice bots; implement complex conversational flows and NLP training; integrate APIs and CRM systems (Salesforce, RingCentral); build multi-language deployments; write optimized Node.js/Python code with unit tests and code reviews; work in Agile teams using JIRA. Must hold a conversational AI platform certification and work IST 2pm–11pm shift.

The AI Architect will design, implement, and oversee large-scale AI systems, ensuring they align with business goals and ethical standards. Responsibilities include architectural design, integration of AI models, and collaboration with data engineering. Requires over 10 years of experience in AI/ML implementations for large enterprises.

The AI Engineer will develop, implement, and maintain AI systems and algorithms, ensuring data quality and deploying models in production. They will collaborate with teams to build data pipelines and ensure AI solutions meet requirements.

Design and maintain scalable Java and Spring Boot backend services, REST APIs, microservices, and event-driven systems. Build cloud-native applications using Docker, Kubernetes, and AWS while ensuring reliability, security, observability, and performance. Use AI-assisted development tools for coding, testing, debugging, documentation, and reviews. Participate in architecture discussions, production support, incident response, and on-call rotations. Collaborate across engineering and product teams and mentor other engineers.

Lead a hands-on AI-first engineering pod to design, build, and ship agentic orchestration pipelines integrated with Slack, issue trackers, Git and CI/CD. Implement AI-assisted development responsibly, improve CI/CD and quality gates, modernize legacy .NET Core 2.1, mentor engineers, codify playbooks and guardrails, and ensure fintech-grade security and auditability.

Lead architecture and engineering for multi-agent and LLM orchestration systems. Build and maintain agentic stacks (CI/CD, testing, monitoring, deployments), create reusable agent components and prompt templates, evaluate LLM providers, mentor engineers, and produce runbooks and operational playbooks across multiple client implementations.

Develop and optimize deep learning, graph, and ML techniques for GPU architectures; work with customers to deliver AI solutions; collaborate with architecture, research, libraries, and system software teams.

Build, test, and deploy AI-powered applications using LLMs and RAG. Develop REST APIs and microservices, implement ingestion, preprocessing, and indexing pipelines, integrate cloud and LLM providers, evaluate model performance, and follow CI/CD, testing, and observability best practices.

Lead architecture and delivery of enterprise AI platforms and products. Define AI standards, drive GenAI adoption, build scalable MLOps and observability, mentor engineers, and guide technical strategy.

Lead technical architecture and hands-on development for multi-agent and LLM orchestration systems. Set engineering standards, build reusable agent components, manage CI/CD and MLOps, evaluate LLM providers, mentor engineers, collaborate on cross-timezone architecture, and produce operational documentation for agentic AI deployments across multiple client implementations.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

Design, build and deploy production AI agents, workflows and data integrations on Microsoft AI platform. Collaborate with stakeholders, implement DevOps/MLOps, monitor models, ensure governance and reusable patterns, and continuously improve solutions.