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.

Edit paid ads, social videos, UGC, product videos, podcasts, and website assets from raw footage through final delivery. Create fast-paced, high-retention short-form content, repurpose long-form footage, and produce multiple ad variations for testing. Use AI tools for voiceovers, captions, transcript editing, audio cleanup, reframing, clipping, and creative variations. Collaborate with social and creative teams to improve performance and stay current with AI editing tools and video trends.

Leads conversational AI engineering teams, including hiring, coaching, delivery planning, stakeholder management, and technical documentation. Oversees Cognigy and Amazon Lex/Amazon Q chatbot and voice-bot implementations, conversational design, NLU/LLM practices, testing, monitoring, integrations, reliability, observability, incident response, security, and compliance.

Develop and support enterprise AI and software solutions end to end, from technology selection and architecture through development, deployment, infrastructure management, CI/CD, database administration, identity integration, and production support. Build LLM, RAG, and agentic AI applications; establish engineering standards and documentation; troubleshoot across application and infrastructure layers; and enable collaborative development at scale.

Build and deploy production AI automations across accounting, compliance, sales, marketing, HR, and finance. The role covers discovery, coding, deployment, monitoring, debugging, support, ROI measurement, and business handover. The engineer will develop LLM-based systems, integrations, agents, and workflows using Python, APIs, databases, warehouses, and business tools. They will partner with non-technical stakeholders, independently own systems end to end, train business users, maintain infrastructure, and identify high-value automation opportunities.

Develop and integrate enterprise cloud software services using Python, Generative AI, serverless technologies, CI/CD, and Infrastructure as Code. Build automated IT operations solutions with AWS, Azure, Ansible, Terraform, Jenkins, and Azure DevOps. Contribute to cloud governance, security, compliance, testing, code quality, and operational standardization. Collaborate with architecture, engineering, cloud operations, and security teams while driving projects, documenting implementations, and reporting progress to stakeholders. Occasional domestic and international travel is required.

Lead the design and production delivery of agentic AI systems, translating domain expertise into ontologies, tool contracts, evaluation sets, and reliable orchestration patterns. Own context management, MCP servers, guardrails, tracing, regression testing, and observability. Direct technical strategy for an engineering pod, establish reusable architecture, and communicate effectively with engineers, domain experts, executives, security teams, and clients.

Builds ontologies, knowledge graphs, and structured tool interfaces that make Cadence simulation products accessible to AI agents. Responsibilities include modeling product capabilities, connecting APIs and data layers, implementing retrieval and context systems, translating simulation workflows into callable operations, and reviewing ontologies for agent readiness across multiple products.

Develops ontology schemas, knowledge graphs, structured tool interfaces, connectors, and retrieval layers that make simulation products accessible to AI agents. The role translates product APIs, documentation, data structures, and workflows into callable operations, collaborating with domain engineers across multiple products. Responsibilities include agentic AI integration, semantic modeling, API development, and reviewing product ontologies for agent readiness.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Own product features end-to-end, including architecture, integrations, technical specifications, deployment, testing, and production reliability. Use AI agents as the primary development tool, delegate implementation, critically review generated code, and ensure financial transaction reliability. Work independently as an individual contributor while helping evolve the company’s AI-first development process.

Designs end-to-end AI and generative AI solutions, leads RFP responses, develops proposals and sales collateral, conducts client workshops, and engages executive stakeholders. The role establishes AI governance, compliance, and observability frameworks; partners with engineering teams on delivery; and manages and mentors solution architects, data scientists, and engineers. It requires strong expertise in GenAI stacks, RAG, multi-agent systems, LLMOps, responsible AI, consulting, and enterprise pre-sales.

Build and operate production AI systems for financial products, including LLM pipelines, RAG, document extraction, anomaly detection, model serving, and automated reconciliation. Develop reliable backend integrations, vector search infrastructure, monitoring, evaluation, observability, and human-in-the-loop workflows. Collaborate with product, backend engineering, and data science teams to establish scalable AI engineering practices.

Build and deploy production-ready Generative AI and LLM applications, including RAG systems, chatbots, copilots, intelligent automation, and agentic workflows. Develop Python backend services, integrate LLM APIs, optimize model quality and performance, implement evaluation and guardrails, and troubleshoot deployed systems. Collaborate with engineering, product, operations, and business teams in a globally distributed environment while translating AI capabilities into scalable production solutions.

Design, develop, deploy, and optimize production-grade Generative AI and LLM-powered systems. Build agentic pipelines with orchestration, tool integration, feedback loops, asynchronous processing, and event-driven architectures. Collaborate across operations, marketing, customer experience, and product teams to identify AI opportunities. Troubleshoot systems, maintain documentation, communicate technical findings, and deliver scalable solutions using Python, cloud platforms, modern AI frameworks, and containerization.

Lead client advisory, solution design, delivery, and productization of AI-powered workflows and automation. Build GenAI solutions, integrations, document-processing systems, and secure knowledge assistants across business platforms. Develop reusable accelerators and service offerings, monitor production performance, improve accuracy and cost, and identify new client opportunities. The role requires hands-on experience with LLMs, RAG, agent frameworks, APIs, Python or TypeScript, business systems, AI security, and production deployment.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Annotate first-person industrial and commercial videos by segmenting actions, assigning timestamps and labels, and describing hands, tools, objects, and workflows. Review and correct model-generated annotations, ensure complete and consistent coverage, resolve unclear cases, and meet quality and productivity targets. Prior hands-on experience with Physical AI, robotics, or industrial egocentric video annotation is mandatory.

Own quality for AI products including a support chatbot, API Copilot, and MCP server. Build golden datasets, evaluation harnesses, scoring systems, regression coverage, and release gates. Analyze production traces and tool calls to identify hallucinations, retrieval, generation, tool-use, context, and PII failures. Define quality metrics, investigate root causes, and convert findings into actionable engineering evidence. The role requires Python, API testing, AI evaluation experience, and strong QA/SDET expertise.

Lead the design and production deployment of LLM-powered AI agents, generative AI systems, and retrieval-augmented generation infrastructure. Build and fine-tune models, develop multi-step agents, apply data science methods, and establish evaluation, monitoring, observability, and feedback systems. Partner with product, backend, and infrastructure teams to scale AI capabilities across messaging, scheduling, sales, and operations while influencing the AI roadmap and mentoring engineers.

Supports and improves the reliability, scalability, and observability of AI tooling infrastructure running on AWS Kubernetes. Responsibilities include incident response, production troubleshooting, automation, deployment and monitoring improvements, dashboard and alert development, distributed systems support, Kafka integration, capacity planning, and operational excellence. The role collaborates with global engineering, infrastructure, and business teams across regions.

Own AI-driven software solutions end to end, from requirements discovery and architecture through development, testing, deployment, and support. Build cloud-native backends in Python, modern Angular or React frontends, and Azure infrastructure. Integrate data science models and AI agents into production systems, analyze business processes, lead validation and testing, provide documentation and technical support, and perform project planning and estimation.

Lead architecture for enterprise AI initiatives across products, platforms, and shared capabilities. Define AI architecture standards, reference patterns, governance, security, and operational guardrails. Guide solutions involving LLM applications, retrieval systems, agentic workflows, orchestration, evaluation, observability, and cloud-native platforms. Partner with business, product, and engineering leaders, influence strategic decisions, mentor technical teams, and support AI-enabled software development and platform modernization.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.