Senior AIML Engineer Generative AI Agentic AI

PwC; Hyderabad, Pune District, Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

PwC Hyderabad seeks a seasoned Full-Stack & Cloud Engineer focused on AI-driven products. You will lead end-to-end ML/LLM projects, architect agent-based solutions, and integrate with enterprise AI platforms.

Strong production experience across ML life cycle is required, with cloud-native deployments on AWS/Azure/GCP and robust MLOps practices. The role demands hands-on coding in Python/JavaScript, REST APIs, microservices, and modern front-end frameworks.

Qualifications

• Strong hands-on experience in ML, DL, Generative AI, and Agentic AI with 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.
• Strong 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.

Responsibilities

• Strong hands-on experience in ML, DL, 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.
• Strong 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.

Skills

Machine Learning

Deep Learning

Generative AI

Agentic AI

LLM-powered apps

RAG architectures

AI agents & multi-agent systems

agent orchestration

MLOps

DevOps practices

Tools

LangGraph

LangChain

CrewAI

AutoGen

LangSmith

Langfuse

Ragas

Arize Phoenix

API integration

Job description

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.
• Strong 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.