AIML Engineer - Agentic AI

Job Title: AI/ML Engineer – Agentic AI

Technical Skill Set

1. Core Programming Skills

• Strong expertise in Python for machine learning, orchestration, and agent development.
• Knowledge of asynchronous programming, concurrency, and task scheduling.

2. Agentic AI

• Experience designing autonomous AI agents for multi-step reasoning, planning, and task orchestration.
• Knowledge of ReAct, Plan-and-Execute, and multi-agent architectures.
• Experience building tool-augmented and function-calling agents.
• Understanding of agent memory management and stateful workflows.

3. Large Language Models (LLMs)

• Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or open-source LLMs.
• Strong prompt engineering skills, including few-shot and zero-shot prompting.
• Understanding of model selection, latency, cost, and context management.
• Knowledge of fine-tuning and LoRA is an advantage.

4. Agent Frameworks

• Hands-on experience with LangGraph and LangChain.
• Familiarity with Semantic Kernel, AutoGen, CrewAI, or similar frameworks.
• Ability to develop custom agent orchestration workflows.

5. RAG, Memory, and Knowledge Management

• Experience implementing Retrieval-Augmented Generation (RAG).
• Knowledge of vector databases such as FAISS, Pinecone, or Azure AI Search.
• Experience with embeddings, document chunking, and context management.
• Knowledge of knowledge graphs or hybrid memory systems is an advantage.

6. Planning and Reasoning

• Experience implementing task planning, replanning, and self-correction workflows.
• Knowledge of guardrails, action validation, failure detection, and recovery mechanisms.

7. MLOps and AgentOps

• Experience deploying AI agents in production environments.
• Knowledge of agent tracing, logging, monitoring, and prompt/model versioning.
• Experience with CI/CD, Docker, Kubernetes, or serverless deployments.

8. Evaluation and Testing

• Ability to evaluate agent performance based on accuracy, task success, latency, cost, and reliability.
• Experience with testing frameworks, simulation environments, and prompt evaluation.

9. Security and Responsible AI

• Understanding of prompt injection, jailbreak prevention, and secure tool execution.
• Knowledge of data privacy, access control, and responsible AI practices.
• Experience implementing human-in-the-loop workflows.

10. Data and Integration

• Experience integrating AI agents with enterprise systems, APIs, databases, and SaaS platforms.
• Working knowledge of SQL/NoSQL databases and event-driven systems.

11. Cloud Platforms

• Strong experience with Azure, AWS, or GCP.
• Knowledge of managed AI services, identity and access management, secrets management, and cost optimization.
• Azure experience is preferred.

12. Good to Have

• Experience building enterprise copilots and autonomous workflows.
• Multi-agent collaboration and orchestration experience.
• Knowledge of reinforcement learning for agent optimization.