Senior AI Engineer: LLM & Agentic Systems Lead

Infosys Limited; Northern (KY); Hybrid; USD 66,000 - 102,000; Full time

Benefits offered by this job

Disability insurance

Health reimbursement accounts

Insurance (various)

401(k) plan

Job summary

Infosys Limited is seeking a Senior AI engineer to design and build production-grade LLM-powered applications and agentic systems. You will own end-to-end development from architecture to deployment, leveraging Python, LangChain, and scalable cloud-based design.

The role focuses on creating RAG pipelines with embeddings, integrating AI with APIs and backend systems, and driving reliable performance and cost-efficient solutions across enterprise data landscapes.

Qualifications

• Senior AI engineer to design and build production-grade LLM-powered applications and agentic systems.
• Experience with Python and modern LLM frameworks.
• Knowledge of RAG architectures, embeddings and vector databases.
• Experience with agentic frameworks (LangChain, LangGraph, AutoGen).
• Strong system design skills for cloud-based applications.
• Willingness to relocate and travel as required.

Responsibilities

• Architect end-to-end LLM-powered applications and agentic workflows using Python.
• Design and implement RAG pipelines over enterprise data using embeddings and vector databases.
• Build multi-step, tool-using agents (planning, execution, memory) using LangChain.
• Integrate AI systems with APIs, backend services, and cloud platforms.
• Establish evaluation, reliability, and performance strategies (accuracy, latency, cost).

Skills

Python

LLM frameworks

RAG architectures

Embeddings

Vector databases

LangChain

LangGraph

AutoGen

Cloud platforms

API integration

System design

Education

Bachelor’s degree

Equivalent experience

Tools

LangChain

REST APIs

Cloud platforms

Job description

Infosys Limited is seeking a Senior AI engineer to design and build production-grade LLM-powered applications and agentic systems. You will own end-to-end development from architecture to deployment, leveraging Python, LangChain, and scalable cloud-based design.

The role focuses on creating RAG pipelines with embeddings, integrating AI with APIs and backend systems, and driving reliable performance and cost-efficient solutions across enterprise data landscapes.