Role & responsibilities

Experience: 5+years | Full-Stack & Cloud Engineering Mandatory

Strong hands-on experience in Machine Learning, Deep Learning, Generative AI, and Agentic AI, with proven production implementations.
Experience building, training, fine-tuning, evaluating, and deploying ML models using PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
Hands-on experience developing LLM-powered applications using Claude, GPT, open-source models, and enterprise AI platforms.
Strong experience implementing RAG architectures, embeddings, vector databases, semantic search, and context engineering.
Proven experience designing and building AI agents and multi-agent systems using LangGraph, LangChain, CrewAI, AutoGen, or equivalent frameworks.
Experience implementing agent orchestration, planning, reasoning, tool calling, memory, state management, and human-in-the-loop workflows.
Hands-on experience with MCP, A2A protocols, API integrations, and enterprise tool connectivity.
Robust understanding of AI agent harness architecture,



including execution loops, context management, tool permissions, guardrails, and error recovery.
Experience building AI evaluation frameworks covering LLM-as-a-Judge, RAG quality, hallucination detection, agent task success, and tool-call accuracy.
Hands-on experience with AI observability and evaluation tools such as LangSmith, Langfuse, Ragas, DeepEval, or Arize Phoenix.
Mandatory full-stack development experience with Python, Java/TypeScript, REST APIs, microservices, and frontend frameworks such as React or Angular.
Mandatory cloud engineering experience with AWS, Azure, or GCP, including Docker, Kubernetes, serverless architectures, and cloud-native deployments.
Strong experience in MLOps, DevOps, CI/CD, Terraform, automated testing, security, and production monitoring for AI applications.
Demonstrated real-world, end-to-end production delivery

📌 Senior AIML Engineer Generative AI Agentic AI (Hyderabad)
🏢 PwC
📍 Hyderabad