AI Systems Engineer

5 days ago

Delhi, Delhi, India beBeeEngineer Full time ₹ 21,73,916 - ₹ 24,36,425
Lead AI Architecture Design

This role involves defining and implementing best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.

As an AI Systems Engineer, you will lead the design of our AI architecture, building multi-stage pipelines using LLM orchestration frameworks (Lang Chain, LangGraph, Llama Index) or custom solutions.

You will develop high-performance services and APIs in Python, Golang, or RUST to support AI workflows, document ingestion, and retrieval processes.

We are looking for a skilled engineer who can implement hybrid search strategies, vector embeddings, and semantic ranking techniques to improve contextual accuracy.

The ideal candidate will have expertise in RAG frameworks, embedding models, and vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).

Familiarity with cloud ML deployment (AWS, Databricks, Azure) and knowledge graph integration with LLMs is a plus.

This role requires strong problem-solving skills, excellent communication, and the ability to work independently while aligning with strategic goals.

Requirements include at least 8 years of software engineering experience, with 2+ years focused on LLMs and retrieval systems.

A Master's or PHD degree in CS is required.

Key Responsibilities:
  • Lead RAG Architecture Design: Define and implement best practices for retrieval-augmented generation systems.
  • Full-Stack AI Development: Build and optimize multi-stage pipelines using LLM orchestration frameworks.
  • Programming & Integration: Develop high-performance services and APIs in Python, Golang, or RUST.
  • Search & Retrieval Optimization: Implement hybrid search strategies, vector embeddings, and semantic ranking techniques.
Skills and Qualifications:
  • Strong proficiency in Python, Golang, or RUST, with experience building high-performance services and APIs.
  • Expertise in RAG frameworks, embedding models, and vector databases.
  • Familiarity with cloud ML deployment (AWS, Databricks, Azure).
  • Masters or PHD degree in CS.

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