Senior AI Engineer GenAI, Agentic AI & Machine Learning

About the RoleAs a Senior AI Engineer, you will lead the development of next-generation AI solutions, including Generative AI, Agentic AI, and Machine Learning, taking them from prototype to production. This role bridges research and real-world impact, where you will also mentor junior engineers while working with LLMs and autonomous AI systems.

Your ResponsibilitiesGenAI & Agentic AI DevelopmentDesign and build autonomous AI agent systems, focusing on multi-agent orchestration, tool-use frameworks, and planning & reasoning architectures (e.g., ReAct, Plan-and-Execute, LangGraph, CrewAI, AutoGen).Architect production-grade GenAI applications, including RAG pipelines, fine-tuning strategies, prompt engineering, guardrails, and evaluation frameworks.Push the boundaries of LLM integration through function calling, structured outputs, multi-modal models, embedding strategies, and vector database design (Pinecone, Weaviate, Qdrant, pgvector).Implement agentic workflows that autonomously reason, retrieve, decide, and act across enterprise systems and data sources.Evaluate and benchmark GenAI/agent solutions rigorously, covering hallucination detection, faithfulness metrics, latency optimization, and cost management.Classical Machine Learning & Data ScienceDevelop and deploy traditional ML models for classification, regression, time-series forecasting, anomaly detection, NLP, and computer vision in industrial use cases.Build end-to-end ML pipelines, encompassing feature engineering, model training, hyperparameter optimization, validation, and serving.Apply the right tool for the job, understanding when classical ML outperforms GenAI and vice versa, and design hybrid solutions combining both paradigms effectively.Champion data quality and feature store practices to ensure reliable, reproducible model performance.MLOps & Production EngineeringOwn the AI/ML infrastructure by designing scalable MLOps pipelines, CI/CD workflows, model registries, and automated retraining loops.Deploy across hybrid environments on-premises, cloud (AWS/Azure/GCP), edge, and air-gapped setups.Implement production-grade observability, including model monitoring, drift detection, A/B testing, logging, and alerting.Leverage DevOps best practices with Kubernetes, Docker, infrastructure-as-code (Terraform/Ansible), and GitHub Actions/GitLab CI.Mentoring & CollaborationGuide and support junior engineers through code reviews, pair programming, and sharing best practices.Act as a technical sparring partner to help less experienced colleagues navigate complex architectural decisions.Coordinate globally as a technical counterpart between Pune engineering and headquarters product/architecture teams.Translate business needs into AI solutions, contributing to AI strategy, roadmaps, and technical decision-making.Share knowledge actively, driving tech talks, documentation, and a culture of continuous learning.Represent Pune engineering expertise in global architecture reviews and technology forums.What You BringGenAI & Agentic AI Expertise (Core Focus)LLM Mastery: Production experience with GPT-4/Claude/Gemini/Llama/Mistral, including fine-tuning, RLHF concepts, quantization, and prompt engineering at scale.Agentic AI: Hands-on experience with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel), multi-agent systems, tool integration, and memory management.RAG Architectures: Advanced retrieval strategies like hybrid search, reranking, chunking optimization, multi-index routing, and evaluation (RAGAS, DeepEval).Vector Databases: Production deployment experience with Pinecone, Weaviate, Qdrant, Milvus, or pgvector.Guardrails & Safety: Experience with output validation, content filtering, hallucination mitigation, and responsible AI practices.Evaluation: Systematic LLM/agent evaluation, automated benchmarks, human-in-the-loop feedback, and cost-performance trade-off analysis.Classical ML & Data Science (Strong Foundation)Core ML: Proven track record with supervised/unsupervised learning, ensemble methods, and deep learning (PyTorch/TensorFlow).Industrial Use Cases: Experience in anomaly detection, predictive maintenance, time-series, NLP, or computer vision.Experimentation: Rigorous approach to hypothesis testing, A/B testing, and model validation.Hybrid Thinking: Ability to architect solutions that combine GenAI with classical ML for maximum value.Engineering & Infrastructure5+ years in IT, with 3+ years focused on AI/ML.Programming: Strong Python (must-have); Go, Java, or Rust are a plus.MLOps: End-to-end pipeline experience including experiment tracking (MLflow/W&B), model serving (TorchServe/Triton/vLLM), and feature stores.Hybrid Deployment: Proven experience with on-premises, edge computing, or air-gapped environments, not just cloud-only.DevOps: Kubernetes, Docker, CI/CD, infrastructure-as-code.Communication & TeamworkNatural mentor: Enjoys sharing knowledge, giving constructive feedback, and helping others grow About the Rol