Job Summary
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 Shape 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 Shape 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 Shape 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) Shape 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) Shape 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 Shape 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) Shape
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 Shape
10. Data & Integration Skills Integration with: Enterprise systems (CRM, ERP, databases) Web services, internal APIs, and SaaS tools Working knowledge
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
Key Responsibilities
Skill Requirements
Python (ML, orchestration)