Senior AI Engineer

Job Title: Senior AI Engineer – Agentic AI & ML Systems (Python Mandatory | AWS Platform)

Location: Hyderabad (Hybrid – 2 days/week from office)

Experience: 10–15 Years

Role Overview

We are looking for high-performing Senior AI Engineers with strong expertise in Agentic AI, MCP servers, and AWS-based ML platforms. This role focuses on building scalable, production-grade AI systems using Python, leveraging LLMs, multi-agent architectures, and cloud-native infrastructure on AWS.

Key Responsibilities

• Design and develop agentic AI systems (multi-agent orchestration, reasoning workflows, tool integrations)
• Build and manage MCP server / model-serving infrastructure
• Develop LLM-powered applications (RAG pipelines, copilots, autonomous agents)
• Build scalable ML pipelines using Python (training, inference, monitoring)
• Deploy and manage solutions on AWS platform (SageMaker, Bedrock, EKS/ECS, EC2, S3, Lambda)
• Optimize systems for performance, scalability, latency, and cost
• Implement evaluation frameworks for LLM accuracy, safety, and grounding
• Integrate AI solutions with enterprise systems and APIs
• Collaborate with DevOps teams for cloud-native deployments
• Mentor team members and drive engineering excellence

Mandatory Skills & Experience

• 10–15 years of experience in AI/ML engineering or data science
• Strong Python expertise (MANDATORY)
• (core programming, ML/AI development, data processing)
• Hands-on experience with LLMs (OpenAI / open-source, prompt engineering, fine-tuning)
• Proven experience building agentic AI / multi-agent systems
• Experience with MCP servers / model-serving frameworks
• Strong experience with RAG pipelines and vector databases
• Hands-on experience working on AWS platform
• (SageMaker, Bedrock, EKS/ECS, EC2, S3, Lambda, IAM)
• Experience with PyTorch / TensorFlow
• Solid understanding of distributed systems and system design

Good to Have

• Experience with LangChain, AutoGen, CrewAI, Semantic Kernel
• Exposure to Amazon Bedrock and GenAI services
• Experience with MLOps tools (MLflow, Kubeflow, SageMaker Pipelines)
• Knowledge of knowledge graphs / semantic search
• Experience in real-time AI / streaming systems