AIML Engineer with Agentic AI

AI/ML Engineer – Technical Skill Set (Agentic AI Focus)

1. Core Programming & Systems Skills

• Python (expert level) for ML, orchestration, and agent logic
• Strong understanding of async programming, concurrency, and task scheduling

2. Foundations of Agentic AI

• Design and implementation of autonomous AI agents capable of:
• Multi‑step reasoning and planning
• Goal decomposition and task orchestration
• Dynamic decision‑making under uncertainty
• Experience with agent architectures:
• ReAct, Plan‑and‑Execute, Reflexive agents
• Hierarchical / multi‑agent systems
• Tool‑augmented and function‑calling agents
• Understanding of stateful vs stateless agents and memory management

3. Large Language Models (LLMs)

• Hands‑on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open‑source models)
• Prompt‑engineering techniques for:
• Reasoning (Chain‑of‑Thought, Self‑Reflection)
• Planning and critique loops
• Instruction following and tool use
• Experience with:
• Few‑shot and zero‑shot prompting
• Model selection trade‑offs (latency, cost, context length)
• Knowledge of fine‑tuning / adapters (LoRA) is a plus

4. Agent Frameworks & Tooling

• Practical experience with agent frameworks, such as:
• LangGraph / LangChain (agents, tools, memory)
• Semantic Kernel
• AutoGen, CrewAI, or similar
• Ability to build custom agent orchestration layers beyond frameworks
• Tool abstraction and execution safety (timeouts, retries, sandboxing)

5. Memory, Context & Knowledge Augmentation

• Design of agent memory systems:
• Short‑term (conversation/state memory)
• Long‑term (episodic, semantic memory)
• Retrieval‑Augmented Generation (RAG):
• Vector databases (FAISS, Pinecone, Azure AI Search, etc.)
• Embedding selection and chunking strategies
• Techniques for context management and compression
• Knowledge graph–augmented or hybrid memory (plus)

6. Planning, Reasoning & Control

• Experience implementing:
• Task planners (step planning, re‑planning)
• Constraint‑based execution
• Feedback and self‑correction loops
• Understanding of:
• Tool reliability scoring
• Guardrails and action validation
• Failure detection and graceful recovery

7. MLOps & AgentOps

• Deployment of agents into production environments
• Observability for agents:
• Tracing agent decisions and tool calls
• Logging prompts, responses, and errors
• Model and prompt versioning
• CI/CD for agent systems
• Experience with Docker, Kubernetes, serverless deployments (Azure/AWS)

8. Evaluation & Testing of Agentic Systems

• Designing evaluation frameworks for agents:
• Task success rate
• Cost, latency, and reliability
• Safety and hallucination detection
• Offline test harnesses and simulation environments
• A/B testing of prompts, tools, and agent strategies

9. Security, Safety & Responsible AI

• Secure tool execution and privilege control
• Prompt‑injection and jailbreak risk mitigation
• Data privacy and isolation in agent memory
• Responsible AI practices:
• Bias awareness
• Explainability of agent decisions
• Human‑in‑the‑loop escalation patterns

10. Data & Integration Skills

• Integration with:
• Enterprise systems (CRM, ERP, databases)
• Web services, internal APIs, and SaaS tools
• Working knowledge of:
• SQL / NoSQL databases
• Event‑driven systems and message queues (plus)

11. Cloud & Platform Expertise

• Strong experience with at least one cloud platform:
• Azure (preferred for enterprise agentic AI), AWS, or GCP
• Managed AI services, identity & access, secrets management
• Cost optimization for LLM‑driven systems

12. Bonus / Advanced Skills (Nice to Have)

• Multi‑agent collaboration and negotiation
• Human‑AI collaboration patterns (copilots, supervisors)
• Reinforcement learning for agent policy optimization
• Experience building enterprise copilots or autonomous workflows