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.
2+ 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.
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