Role: Lead AI Engineer - AI Assistants & Agentic AI

Exp: 3 to 5 Years

We are looking for a highly experienced and hands-on AI Engineer who has built and deployed real-world AI Assistants & Agentic AI and understands deeply how they work internally — including memory management, tool usage, reasoning loops, context orchestration, and multi-agent coordination.

This role requires someone who does not just experiment with LLM APIs but has architected production-grade AI assistants capable of:

· Tool calling / function execution

· Context management & long-term memory

· Retrieval-augmented reasoning

· Goal-based task planning

· Autonomous decision-making

· Multi-step workflow execution

You will lead the design and evolution of next-generation AI Assistants integrated into enterprise automation systems.

What You Will Do

1. Build Advanced AI Assistants

· Design and implement production-grade AI Assistants.

· Develop:

o Tool-augmented agents o Multi-step planners o Self-reflective reasoning systems o Memory-enabled assistants (short-term + long-term)

· Implement function calling, tool orchestration, and action chaining.

· Build assistants capable of interacting with APIs, databases, and enterprise systems.

2. Deep Understanding of Assistant Internals

· Design context window management strategies.

· Implement:

o Conversation memory layers o Persistent vector-based memory o Context compression strategies

· Reduce hallucinations via:

o Grounded retrieval o Tool validation o Guardrails

· Architect reliable assistant behavior in enterprise settings.

3. Agentic & Multi-Agent Systems

· Design goal-driven AI agents.

· Build multi-agent workflows using:





o LangGraph o LangChain o LlamaIndex o CrewAI (or similar)

· Implement:

o Task decomposition o Agent-to-agent communication o Delegation & planning loops

· Improve agent determinism and traceability.

4. RAG & Knowledge Systems

· Architect scalable Retrieval-Augmented Generation pipelines.

· Work with vector databases (ChromaDB, FAISS, Weaviate, Milvus).

· Implement hybrid search and reranking.

· Design structured & unstructured ingestion pipelines.

5. LLM Optimization & Fine-Tuning

· Fine-tune and optimize Small/Tiny LLMs (Phi-3, Mistral, Llama 3, etc.).

· Apply LoRA, QLoRA, PEFT techniques.

· Optimize inference for low-latency AI assistants.

· Implement model routing and fallback strategies.

Required Skills:

· 3–6 years in AI/ML/NLP.

· Robust expertise in LLMs and transformer-based architectures.

· Hands-on experience building AI Assistants in production.

· Deep understanding of:

o Tool-calling agents o Memory management o RAG pipelines o Context engineering

· Proficiency in Python.

· Experience with:

o Hugging Face o PyTorch o LangChain / LangGraph / LlamaIndex o Vector databases

· Experience deploying AI systems using Docker/Kubernetes.

What We’re Specifically Looking For

We want someone who can confidently answer:

· How does an AI Assistant manage context internally?

· How do you prevent hallucinations in tool-calling agents?

· How do you design long-term memory for assistants?

· How do multi-agent systems coordinate tasks?

· How do you make assistants reliable in production?

This is not a prompt-engineering role. This is a systems-level AI engineering role

📌 AI Engineer (Hyderabad)
🏢 AutomatR
📍 Hyderabad