Artificial Intelligence/Machine Learning Engineer - RAG/LLM

Job Description :

- Lead RAG Architecture Design Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.

- Full-Stack AI Development Build and optimize multi-stage pipelines using LLM orchestration frameworks (Lang Chain, LangGraph, Llama Index, or custom).

- Programming & Integration Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.

- Search & Retrieval Optimization Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.

- Prompt Engineering Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.

- Bachelor's degree required

- Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs.

Key Responsibilities :

- Full-Stack AI Development Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom).

- Programming & Integration Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.

- Search & Retrieval Optimization Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.

- Prompt Engineering Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.

- Mentorship & Collaboration Partner with cross-functional teams and guide engineers on RAG and LLM best practices.

- Performance Monitoring Establish KPIs and evaluation metrics for RAG pipeline quality and model performance.

Qualifications :

Must Have :

- 8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems.

- Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs.

- Expertise in RAG frameworks (Lang Chain, LangGraph, Llama Index) and embedding models.

- Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).

- Strong understanding of hybrid search (semantic + keyword) and embedding optimization.

- Bachelor's degree required

Preferred :

- Knowledge graph integration with LLMs.

- Familiarity with cloud ML deployment (AWS (preferred), Databricks, Azure).

- Masters or PHD degree in CS

Soft Skills :

- Strong problem-solving and decision-making skills under tight timelines.

- Excellent communication for cross-functional collaboration.

- Ability to work independently while aligning with strategic goals