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

- Core Programming & Systems Skills

 Python (expert level) for ML, orchestration, and agent logic

 Solid understanding of async programming, concurrency, and task scheduling

- Foundations of Agentic AI

 Design and implementation of autonomous AI agents capable of:

o Multistep reasoning and planning o Goal decomposition and task orchestration o Dynamic decisionmaking under uncertainty

 Experience with agent architectures:

o ReAct, PlanandExecute, Reflexive agents o Hierarchical / multiagent systems o Toolaugmented and functioncalling agents

 Understanding of stateful vs stateless agents and memory management

- Large Language Models (LLMs)

 Handson experience with LLMs (OpenAI, Azure OpenAI, Anthropic, opensource models)

 Promptengineering techniques for:

o Reasoning (ChainofThought, SelfReflection)

o Planning and critique loops o Instruction following and tool use

 Experience with:

o Fewshot and zeroshot prompting o 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:

o LangGraph / LangChain (agents, tools, memory)

o Semantic Kernel o AutoGen, CrewAI, or similar

 Ability to build custom agent orchestration layers beyond frameworks

 Tool abstraction and execution safety (timeouts, retries, sandboxing)

Classification

Internal

- Memory, Context & Knowledge Augmentation

 Design of agent memory systems:

o Shortterm (conversation/state memory)

o Longterm (episodic, semantic memory)

 RetrievalAugmented Generation (RAG):

o Vector databases (FAISS, Pinecone, Azure AI Search, etc.)

o Embedding selection and chunking strategies

 Techniques for context management and compression

 Knowledge graph–augmented or hybrid memory (plus)

- Planning, Reasoning & Control





 Experience implementing:

o Task planners (step planning, replanning)

o Constraintbased execution o Feedback and selfcorrection loops

 Understanding of:

o Tool reliability scoring o Guardrails and action validation o Failure detection and graceful recovery

- MLOps & AgentOps

 Deployment of agents into production environments

 Observability for agents:

o Tracing agent decisions and tool calls o 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:

o Task success rate o Cost, latency, and reliability o Safety and hallucination detection

 Offline test harnesses and simulation environments

Classification

Internal

 A/B testing of prompts, tools, and agent strategies

- Security, Safety & Responsible AI

 Secure tool execution and privilege control

 Promptinjection and jailbreak risk mitigation

 Data privacy and isolation in agent memory

 Responsible AI practices:

o Bias awareness o Explainability of agent decisions o Humanintheloop escalation patterns

- Data & Integration Skills

 Integration with:

o Enterprise systems (CRM, ERP, databases)

o Web services, internal APIs, and SaaS tools

 Working knowledge of:

o SQL / NoSQL databases o Eventdriven systems and message queues (plus)

- Cloud & Platform Expertise

 Strong experience with at least one cloud platform:

o Azure (preferred for enterprise agentic AI), AWS, or GCP

 Managed AI services, identity & access, secrets management

 Cost optimization for LLMdriven systems

- Bonus / Advanced Skills (Nice to Have)

 Multiagent collaboration and negotiation

 HumanAI collaboration patterns (copilots, supervisors)

 Reinforcement learning for agent policy optimization

 Experience building enterprise copilots or autonomous workflows

📌 AI/ML Engineer – (Agentic AI Focus) (Hyderabad)
🏢 HCL INDIA
📍 Hyderabad