Senior Agentic AI Tech Lead

CEI; Chester, Northern (Delaware County, KY); Hybrid; USD 131,000 - 193,000; Full time

Job summary

CEI seeks a Senior Agentic AI Tech Lead to design, build, and deploy enterprise-grade AI agents and intelligent automation solutions. You will focus on agentic AI applications, Retrieval-Augmented Generation (RAG) systems, LLM-powered workflows, and scalable AI platforms using Python and modern AI frameworks.

The ideal candidate has hands-on experience building production AI systems, integrating LLMs, orchestrating multi-agent workflows, and deploying cloud-native applications that support

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field.
• 8+ years of software development experience with strong proficiency in Python.
• Experience deploying Generative AI, LLM, or Agentic AI solutions in production environments.
• Experience developing RAG architectures and integrating vector databases.
• Strong understanding of prompt engineering, embeddings, semantic search, and LLM evaluation techniques.
• Experience building APIs and microservices using Python frameworks such as FastAPI.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with containerization and deployment technologies including Docker and Kubernetes.

Responsibilities

• Design, develop, and deploy agentic AI applications using modern LLM frameworks and orchestration platforms.
• Build Retrieval-Augmented Generation (RAG) solutions leveraging enterprise data sources, vector databases, and semantic search technologies.
• Develop AI agents capable of tool calling, workflow automation, reasoning, and decision support.
• Create scalable APIs and backend services to support AI-enabled products and applications.
• Design and implement document ingestion, knowledge retrieval, embedding generation, and context management pipelines.
• Collaborate with product, engineering, and business teams to identify and deliver AI-driven solutions.
• Evaluate, test, and optimize LLM performance, response quality, latency, and reliability.
• Implement monitoring, observability, security, and governance controls for enterprise AI systems.
• Participate in architecture discussions, proof-of-concept development, and technical design reviews.
• Create and maintain technical documentation, implementation plans, and best practices for AI development.

Skills

Python

LLM deployment

RAG architectures

APIs & microservices

Cloud platforms

Docker

Kubernetes

FastAPI

Vector databases

Prompt engineering

Education

Bachelor's degree in Computer Science / related field

Tools

Docker

Kubernetes

FastAPI

LangChain

LangGraph

OpenAI

Azure OpenAI

Vector databases

Job description

Back Senior Agentic AI Tech Lead

Other West Chester, PA Contract On-Site Sep 25, 2026

Role: Senior Agentic AI Tech Lead

Location: Remote

Schedule: EST Hours

Rate: $100/hour W2

We are seeking a Senior Agentic AI Tech Lead to design, build, and deploy enterprise-grade AI agents and intelligent automation solutions. This role will focus on developing agentic AI applications, Retrieval-Augmented Generation (RAG) systems, LLM-powered workflows, and scalable AI platforms using Python and modern AI frameworks. The ideal candidate has hands‑on experience building production AI systems, integrating Large Language Models (LLMs), orchestrating multi‑agent workflows, and deploying cloud‑native applications that support real‑world business processes.

Responsibilities

• Design, develop, and deploy agentic AI applications using modern LLM frameworks and orchestration platforms.
• Build Retrieval-Augmented Generation (RAG) solutions leveraging enterprise data sources, vector databases, and semantic search technologies.
• Develop AI agents capable of tool calling, workflow automation, reasoning, and decision support.
• Create scalable APIs and backend services to support AI-enabled products and applications.
• Design and implement document ingestion, knowledge retrieval, embedding generation, and context management pipelines.
• Collaborate with product, engineering, and business teams to identify and deliver AI-driven solutions.
• Evaluate, test, and optimize LLM performance, response quality, latency, and reliability.
• Implement monitoring, observability, security, and governance controls for enterprise AI systems.
• Participate in architecture discussions, proof-of-concept development, and technical design reviews.
• Create and maintain technical documentation, implementation plans, and best practices for AI development.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field.
• 8+ years of software development experience with strong proficiency in Python.
• Experience building and deploying Generative AI, LLM, or Agentic AI solutions in production environments.
• Experience developing RAG architectures and integrating vector databases.
• Strong understanding of prompt engineering, embeddings, semantic search, and LLM evaluation techniques.
• Experience building APIs and microservices using Python frameworks such as FastAPI.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with containerization and deployment technologies including Docker and Kubernetes.
• Strong troubleshooting, debugging, and problem‑solving skills.

Preferred Qualifications

• Experience with LangChain, LangGraph, CrewAI, AutoGen, or similar agent orchestration frameworks.
• Experience integrating AI agents with enterprise systems, databases, APIs, and workflow platforms.
• Familiarity with OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock, or other enterprise LLM platforms.
• Experience with MLOps, LLMOps, and AI governance practices.
• Experience developing multi‑agent systems and autonomous workflow solutions.
• Exposure to Go or other backend programming languages.

Technical Environment

• Python
• FastAPI
• LangChain / LangGraph
• OpenAI, Claude, Azure OpenAI, Amazon Bedrock
• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Docker & Kubernetes
• AWS / Azure / GCP
• REST APIs & Microservices
• Git, CI/CD Pipelines
• LLMOps & Monitoring Frameworks