AI/ML Engineer (LLM & Agent Systems)
Budget: $50 – $0 USD
Project Overview
This project focuses on building:
LLM-powered systems that take actions, not just generate outputs
Autonomous agent architectures that interact with tools, APIs, and data pipelines
A scalable AI system capable of real-world execution and decision-making
What You’ll Be Building
LLM-powered applications (OpenAI, Anthropic, LLaMA, Mistral)
RAG pipelines (retrieval, hybrid search, re-ranking)
Agent systems using LangChain / LangGraph
Tool-use systems (API integrations, external tools)
Data ingestion + retrieval pipelines
Monitoring, logging, and evaluation systems
Key Responsibilities
Architect and develop scalable AI systems
Design and optimize RAG pipelines
Build and manage multi-agent workflows
Improve latency, cost, and performance
Implement evaluation frameworks for LLM outputs
Work across backend, infrastructure, and AI layers
Required Skills
Strong Python + production experience
Hands-on with LLMs (OpenAI, Anthropic, etc.)
Experience with:
RAG systems & embeddings
Vector databases (FAISS, Pinecone, Weaviate, Milvus)
LangChain / LangGraph / LlamaIndex
Solid understanding of:
APIs & microservices
Distributed systems
Nice to Have
Multi-agent systems experience
Fine-tuning / RLHF knowledge
Real-time / async systems
Evaluation metrics (NDCG, BLEU, etc.)
Docker, Kubernetes, CI/CD
Search / recommendation systems background
What We’re Looking For
Someone who thinks in systems, not just models
Builder mindset — able to go from concept → production
Focus on real-world execution, not just theory
Comfortable working in a fast-moving startup environment
To Apply
Please include:
Portfolio / GitHub /Linkedin Profile
Relevant AI/LLM projects
Brief explanation of your experience with agent systems or RAG
This project focuses on building:
LLM-powered systems that take actions, not just generate outputs
Autonomous agent architectures that interact with tools, APIs, and data pipelines
A scalable AI system capable of real-world execution and decision-making
What You’ll Be Building
LLM-powered applications (OpenAI, Anthropic, LLaMA, Mistral)
RAG pipelines (retrieval, hybrid search, re-ranking)
Agent systems using LangChain / LangGraph
Tool-use systems (API integrations, external tools)
Data ingestion + retrieval pipelines
Monitoring, logging, and evaluation systems
Key Responsibilities
Architect and develop scalable AI systems
Design and optimize RAG pipelines
Build and manage multi-agent workflows
Improve latency, cost, and performance
Implement evaluation frameworks for LLM outputs
Work across backend, infrastructure, and AI layers
Required Skills
Strong Python + production experience
Hands-on with LLMs (OpenAI, Anthropic, etc.)
Experience with:
RAG systems & embeddings
Vector databases (FAISS, Pinecone, Weaviate, Milvus)
LangChain / LangGraph / LlamaIndex
Solid understanding of:
APIs & microservices
Distributed systems
Nice to Have
Multi-agent systems experience
Fine-tuning / RLHF knowledge
Real-time / async systems
Evaluation metrics (NDCG, BLEU, etc.)
Docker, Kubernetes, CI/CD
Search / recommendation systems background
What We’re Looking For
Someone who thinks in systems, not just models
Builder mindset — able to go from concept → production
Focus on real-world execution, not just theory
Comfortable working in a fast-moving startup environment
To Apply
Please include:
Portfolio / GitHub /Linkedin Profile
Relevant AI/LLM projects
Brief explanation of your experience with agent systems or RAG