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

1. Core Programming & Systems Skills

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

📌 AI/ML Engineer – Technical Skill Set (Agentic AI Focus) (India)
🏢 WebSenor
📍 India