Overview

AI/ML Engineer Agentic AI (contract)

Responsibilities

• Design, develop, and deploy autonomous AI agents for enterprise use cases.
• Build multi-step reasoning, planning, task orchestration, and workflow automation.
• Develop scalable AI applications using Python and modern Agentic AI frameworks.
• Build RAG pipelines using vector databases and embedding models.
• Integrate AI agents with enterprise applications, APIs, databases, CRM/ERP systems, and SaaS platforms.
• Design short-term and long-term memory architectures for conversational agents.
• Implement planning, feedback loops, self-correction, guardrails, and failure-recovery mechanisms.
• Develop secure tool-calling capabilities with validation, retries, timeouts, and access controls.
• Deploy AI applications using Docker, Kubernetes, and cloud-native services.
• Build evaluation frameworks to measure task success, latency, cost, safety, and hallucination rates.
• Implement Responsible AI practices, data privacy, explainability, and human-in-the-loop workflows.
• Collaborate with cross-functional teams to deliver scalable enterprise AI solutions.

Qualifications

• Location: Hyderabad Hybrid (5 Days Working) / Remote
• Work Timings: 11:00 AM 9:00 PM
• Job Summary
• We are seeking an experienced AI/ML Engineer specializing in Agentic AI to design, develop, deploy, and support enterprise-grade autonomous AI systems powered by Large Language Models (LLMs).
• Programming & System Design
• Expert-level Python programming.
• Asynchronous programming, concurrency, and task scheduling.
• Scalable backend services and REST APIs.
• Autonomous AI agents.
• Multi-step reasoning and planning.
• Goal decomposition and workflow orchestration.
• ReAct and Plan-and-Execute patterns.
• Multi-agent and hierarchical agent architectures.
• Tool calling and function execution.
• Stateful and stateless agent design.
• Feedback loops and failure recovery.
• Azure OpenAI
• Open-source LLMs
• Prompt engineering
• Few-shot and zero-shot prompting
• Model selection based on latency, context window, and cost
• LoRA/model fine-tuning is an advantage
• Agent Frameworks
• Semantic Kernel
• Custom AI orchestration frameworks
• Azure AI Search
• Embedding models
• Chunking strategies
• Context compression
• Knowledge Graph integration is preferred

Nice to have

• Multi-agent collaboration systems
• Enterprise Copilot development
• AI Supervisor architectures
• Human-AI collaboration workflows
• Reinforcement Learning
• Autonomous workflow automation
• Knowledge Graph implementations
• Required Experience
• 68 years of Software Engineering or AI/ML development experience.
• Strong hands-on experience building production AI applications.