Job Description

We are looking for highly skilled

NLP & LLM Experts

to join our AI team and drive innovation in

Agentic AI systems

, focusing on reasoning, memory management, tool use, and multi-agent orchestration. This role is ideal for individuals who are passionate about building intelligent systems that go beyond traditional LLM applications -- leveraging

LangGraph

,

Model Context Protocol (MCP)

,

Retrieval-Augmented Generation (RAG)

, and

modular AI agents

to power dynamic, context-aware applications.


You'll help shape the next generation of

intelligent automation

across domains such as customer support, internal tooling, and knowledge orchestration.

Key Responsibilities

Design and implement modular

AI agents

using frameworks such as

LangGraph

, enabling multi-turn reasoning, tool usage, and context retention. Build agentic workflows with

Model Context Protocol (MCP)

to manage dynamic memory, context injection, and action coordination. Develop and fine-tune

LLMs

(e.g., GPT-4, Qwen, LLaMA, Mistral) for downstream tasks such as multi-agent collaboration, structured generation, and reasoning. Integrate

Retrieval-Augmented Generation (RAG)

architectures with vector databases (e.g., FAISS, Milvus) to support context-aware responses and tool routing. Engineer prompts and

multi-step reasoning chains

for LLMs in agent-based environments. Deploy and optimize open-source LLMs using

vLLM

,

Triton

, or

Ollama

, ensuring low-latency inference at scale. Translate core NLP capabilities into production-ready agent behaviors, collaborating with engineering and product teams. Stay at the forefront of

agentic AI research

, contributing to internal frameworks and incorporating cutting-edge ideas from open source and academia. Present project outcomes and model behaviors to both technical and non-technical stakeholders to guide product strategy.

Basic Qualifications

Bachelor's, Master's, or PhD in

Computer Science

,

Artificial Intelligence

, or a related field. 5+ years of experience in

AI or NLP-focused roles

, including production deployment of LLM-driven systems. Proficiency in

Python

and libraries such as

Transformers

,

LangGraph

,

LangChain

, and

PyTorch

. Strong knowledge of

prompt engineering

,

context management

, and

multi-agent orchestration

. Hands-on experience deploying and scaling LLMs in production environments. * Familiarity with

retrieval mechanisms

,

vector search

, and

memory architectures

in agent workflows.