AI/ML Engineer – Agentic AI Job Summary: We are looking for an AI/ML Engineer – Agentic AI with strong expertise in Python, LLMs, LangGraph/LangChain, RAG, multi-agent systems, and prompt engineering. The role focuses on building and deploying autonomous AI agents, enterprise copilots, and intelligent workflows using Azure/AWS, Docker, Kubernetes, MLOps, and AgentOps, with a robust focus on scalability, security, observability, and responsible AI. Technical Skill Set – Agentic AI 1. Core Programming & Systems Expert-level Python for ML, orchestration, and agent development Strong understanding of asynchronous programming, concurrency, and task scheduling 2. Agentic AI Experience designing autonomous AI agents for multi-step reasoning, planning, task orchestration, and dynamic decision-making Knowledge of ReAct, Plan & Execute, Reflexive, hierarchical, and multi-agent architectures Experience with tool-augmented and function-calling agents Understanding of stateful/stateless agents and memory management 3. Large Language Models (LLMs) Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or open-source LLMs Strong prompt engineering skills Experience with few-shot/zero-shot prompting, reasoning, planning, critique loops, and tool usage Understanding of model selection based on latency, cost, and context requirements Knowledge of fine-tuning/LoRA is a plus 4. Agent Frameworks & Tooling Hands-on experience with LangGraph/LangChain Exposure to Semantic Kernel, AutoGen, CrewAI, or similar frameworks Ability to build custom agent orchestration layers Experience with tool abstraction, execution safety, retries, timeouts, and sandboxing 5. Memory,



Context & RAG Experience designing short-term and long-term agent memory Strong RAG implementation experience Knowledge of vector databases such as FAISS, Pinecone, Azure AI Search, etc. Understanding of embeddings, chunking strategies, context management, and compression Knowledge of knowledge-graph or hybrid memory approaches is a plus 6. Planning, Reasoning & Control Experience implementing task planning and re-planning Constraint-based execution and feedback/self-correction loops Understanding of guardrails, action validation, failure detection, and recovery mechanisms 7. MLOps & AgentOps Experience deploying AI agents into production Agent observability, tracing, logging, and monitoring Model and prompt versioning CI/CD for AI/agent systems Hands-on experience with Docker, Kubernetes, Azure/AWS, or serverless deployments 8. Evaluation & Testing Experience evaluating agent systems based on task success, cost, latency, reliability, and safety Experience with offline test harnesses and simulation environments A/B testing of prompts, tools, and agent strategies 9. Security & Responsible AI Secure tool execution and privilege control Understanding of prompt injection and jailbreak mitigation Data privacy and isolation in agent memory Responsible AI practices, explainability, and human-in-the-loop escalation 10. Data & Integration Integration with enterprise systems, CRM, ERP, databases, APIs, and SaaS platforms Working knowledge of SQL/NoSQL databases Exposure to event-driven systems and message queues is a plus 11. Cloud & Platform Strong expertise in at least one cloud platform: Azure, AWS, or GCP Experience with managed AI services, IAM, secrets management, and security Understanding of LLM cost optimization 12. Nice to Have Multi-agent collaboration and negotiation Enterprise copilots and autonomous workflows Human-AI collaboration patterns Reinforcement learning for agent policy optimization

📌 AIML with Agentic AI (Hyderabad)
🏢 Prophecy Technologies
📍 Hyderabad