Top AI & Machine Learning Jobs in Pune, MH

Designs, builds, and operates production-grade agentic AI systems and generative AI applications. Responsibilities include tool-calling infrastructure, Python backend services, agent orchestration, memory and retrieval systems, observability, LLMOps/MLOps pipelines, multimodal AI, model adaptation, responsible AI, and production evaluation. The role also mentors engineers and establishes engineering standards for enterprise generative AI development.

Lead architecture and technical strategy for an enterprise generative AI platform supporting agentic systems, tool calling, multi-agent orchestration, memory, retrieval, inference, LLMOps, observability, security, and responsible AI. Define reusable standards, evaluate models and platforms, guide architecture decisions, collaborate with engineering, product, security, and data teams, mentor technical leaders, and build prototypes for high-scale, reliable production systems.

Leads and develops a Data Science team, oversees predictive and machine learning model delivery, establishes governance and lifecycle standards, and partners with business, engineering, and analytics leaders. Translates complex data into executive insights, frames ambiguous business problems, improves analytical processes, ensures production-ready models, and measures business impact while developing technical talent and promoting responsible, explainable analytics.

Develop generative AI and machine learning solutions for anti-money-laundering transaction monitoring. Responsibilities include building RAG and agentic workflows, fine-tuning foundation models, creating multimodal and computer vision applications, evaluating model quality and bias, implementing AI observability, and deploying secure, scalable systems on cloud and data platforms. The role also contributes to MLOps, production integration, research adoption, and collaboration with technical and non-technical stakeholders.

Leads customer-embedded AI discovery and rapid proof-of-concept development for issuer and acquirer clients. Builds agentic pipelines, conversational interfaces, and analytical tools to validate business hypotheses, gathers customer feedback, and translates findings into evidence-backed product briefs and roadmap recommendations. Partners with product and engineering teams, identifies repeatable platform opportunities, supports pre-sales discussions, and mentors engineers and analysts on customer discovery and PoC scoping.

Own production operations for AI agents, including monitoring accuracy, latency, SLAs, drift, guardrails, and reliability. Provide L1/L2 support, investigate traces and root causes, evaluate reasoning and retrieval quality, manage API and authentication issues, and partner with AI Engineering on prompt, routing, and context improvements. Embed governance, approval workflows, and audit logging into the agent learning lifecycle.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Lead ML engineering for Operational Intelligence: design and productionize multimodal transformer and agentic systems, build scalable RAG/Graph-RAG and LLMOps/MLOps pipelines, implement inference services and memory/state subsystems, apply traditional and deep learning methods, enforce Responsible AI, and drive research-to-production innovation.

Join the AI Innovation at Scale team to design and execute test strategies, perform white-box and black-box testing, validate data using Impala and Spark, monitor Spark job execution, collect logs, analyze quality data, and work with stakeholders to resolve issues and improve QA processes.