Top AI & Machine Learning Jobs in Pune, MH

Lead the machine learning engineering team in building enterprise AI solutions, including multi-agent systems, multimodal pipelines, RAG and Graph-RAG architectures, transformer models, LLMOps/MLOps platforms, and Python inference services. The role also owns model deployment, statistical modeling, responsible AI governance, state and memory systems, continuous retraining, innovation research, and stakeholder management.

Designs and deploys generative AI and machine learning solutions for AML transaction monitoring. Builds RAG, MCP, tool-calling, LLM fine-tuning, multimodal content generation, computer vision, evaluation, observability, and MLOps systems. Develops scalable cloud solutions using AWS, Azure, and Databricks, integrates models into production applications, and ensures performance, security, and compliance. Collaborates with technical and business stakeholders while applying current AI/ML research.

Own the strategy, roadmap, requirements, and delivery of AI-powered financial reconciliation products. Lead discovery with financial stakeholders, translate reconciliation and regulatory needs into BRDs, epics, and user stories, prioritize backlogs, and guide engineering and AI/ML teams from concept through launch. Define performance KPIs, support pricing and go-to-market activities, and ensure compliance, governance, auditability, and financial controls across enterprise reconciliation workflows.

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Design, build, and deploy AI and Generative AI solutions using LLMs, agent frameworks, and RAG. Develop scalable AI/ML pipelines, cloud-native integrations (Azure/Databricks/AWS), and MLOps/LLMOps deployments. Integrate AI with APIs, databases, and internal apps. Implement Responsible AI practices (bias detection, hallucination mitigation, explainability, safety guardrails). Monitor, test, and maintain production AI systems while staying current on Generative AI trends.

Designs, builds, and operates enterprise AI platforms across public and private clouds. Develops MLOps, model deployment, monitoring, observability, governance, automation, and infrastructure capabilities supporting machine learning and generative AI workloads. Responsibilities include troubleshooting production systems, improving reliability and scalability, collaborating with engineering, security, and governance teams, evaluating emerging AI technologies, contributing to architecture and platform strategy, and mentoring peers.

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.

Lead and grow a data science team to deliver predictive and ML-driven insights. Partner with business and engineering to build production-ready models, enforce model governance, ensure explainability, and measure business impact while driving continuous improvement.

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.