AI/ML Engineer - (Agentic AI Focus)

Core Programming & Systems Skills

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

Foundations of Agentic AI

Design and implementation of autonomous AI agents capable of:

Multistep reasoning and planning

Goal decomposition and task orchestration

Dynamic decisionmaking under uncertainty Experience with agent architectures:

ReAct, PlanandExecute, Reflexive agents

Hierarchical / multiagent systems

Toolaugmented and functioncalling agents Understanding of stateful vs stateless agents and memory management

Large Language Models (LLMs)

Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open-source models) Prompt engineering techniques for:

Reasoning (ChainofThought, SelfReflection)

Planning and critique loops

Instruction following and tool use Experience with:

Fewshot and zeroshot prompting

Model selection tradeoffs (latency, cost, context length) Knowledge of finetuning / adapters (LoRA) is a plus

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)

Memory, Context & Knowledge Augmentation

Design of agent memory systems:

Shortterm (conversation/state memory)

Longterm (episodic, semantic memory) RetrievalAugmented 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)

Planning, Reasoning & Control

Experience implementing:

Task planners (step planning, replanning)

Constraint-based execution

Feedback and self-correction loops Understanding of:

Tool reliability scoring

Guardrails and action validation

Failure detection and graceful recovery

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)

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

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

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)

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

Bonus / Advanced Skills (Nice to Have)

Multiagent collaboration and negotiation

Human-AI collaboration patterns (copilots, supervisors)

Reinforcement learning for agent policy optimization

Experience building enterprise copilots or autonomous workflows