AI Engineer

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. · Strong 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