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

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

Work Location: Hybrid remote in Noida, Uttar Pradesh (Noida)