Senior AI Engineer – GenAI, Agentic AI & Machine Learning

About the Role

As 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 Responsibilities

GenAI & Agentic AI Development

• Design 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 Science

• Develop 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 Engineering

• Own 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 & Collaboration

• Guide 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 Bring

GenAI & 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 & Infrastructure

• 5+ 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 & Teamwork

• Natural mentor: Enjoys sharing knowledge, giving constructive feedback, and helping others grow technically.
• Global collaboration: Experience in distributed teams across time zones on headquarters-level projects.
• Stakeholder communication: Ability to translate complex AI concepts for technical and non-technical audiences.
• Delivery track record: Successfully delivered complex projects on time with cross-functional dependencies.
• Autonomous driver: Comfortable navigating ambiguity and owning initiatives end-to-end.

Mindset & Culture Fit

• Pioneering: Passionate about experimenting with emerging AI and the latest agent architectures.
• Customer-Centric: Evaluates AI solutions based on real business value.
• Pragmatic Innovator: Balances cutting-edge exploration with production reliability.
• Collaborative: Thrives in team settings, shares openly, and lifts those around them.
• Growth-Oriented: Invests in colleagues' development through peer mentoring and knowledge sharing.

Bonus Points

• Contributions to open-source AI/ML projects or agent frameworks.
• Published research, blog posts, or conference talks in GenAI/Agentic AI.
• Experience with multi-modal AI (vision-language models, audio, video).
• Familiarity with industrial AI – manufacturing, automation, or IoT contexts.