Artificial Intelligence Engineer

Job Role: AI/ML Engineer (Agentic AI Focus)

Experience: 6 - 8 Years

Location: Hyderabad

Work Mode: 5 Days Onsite

Working Hours: 11 AM 9 PM

Notice Period: Immediate to 15 Days

Email your CV: [email protected]

JD-

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 is a 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 is a plus.

11. Cloud & Platform Expertise

• Strong experience with at least one cloud platform:

• Azure (preferred for enterprise agentic AI).

• AWS.

• GCP.

• Managed AI services, identity & access, and 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.