Agentic AI Developer

TechDigital Group; Charlotte (NC); On-site; USD 120,000 - 170,000; Full time

Job summary

TechDigital Group is seeking a seasoned software/data engineer to build production‑grade AI agents, tools, prompts, evals, and integrations on an established reference architecture. You will implement multi‑agent systems, RAG pipelines, and scalable APIs using Python and modern frameworks.

Ideal candidates have 3–8 years in software or data engineering, hands‑on Generative AI experience, and a strong background in APIs, distributed systems, and cloud services.

Qualifications

• 3–8 years of experience in software development or data engineering.
• Hands-on experience in Generative AI or LLM‑based applications.
• Experience building APIs, microservices, or distributed systems.
• Bachelor's or Master's in CS, AI/ML, Data Science, or related field.

Responsibilities

• Implement agents and sub‑agents (planner, executor, critic, router) using Claude Agent SDK / Lang Graph.
• Build tools and MCP integrations, design clean tool schemas, idempotent operations, and robust error handling.
• Implement RAG pipelines: ingestion, chunking, embedding (Bedrock Titan), hybrid retrieval, citation rendering.
• Develop Fast API/Python services exposing agent capabilities (sync + streaming); integrate with SQL (Postgres) and object stores (S3).
• Write evaluation harnesses (golden sets, regression suites, LLM‑as‑judge) and trace/observe agent runs.
• Implement guardrails: input/output validation, schema enforcement, rate limiting, prompt‑injection defenses.
• Participate in code reviews, pairing, and architecture discussions; own quality of the code you ship.
• Strong Python (FastAPI, async, Pydantic) or Node/TypeScript equivalent.
• Hands‑on with at least one agent framework (Claude Agent SDK / Lang Graph / AutoGen).
• Practical experience with LLM tool/function calling, structured outputs, streaming.
• RAG implementation experience (pgvector / FAISS / OpenSearch).
• Git, CI/CD, containerization (Docker), and cloud basics (AWS preferred).

Skills

Generative AI

LLM applications

APIs and microservices

Python

Distributed systems

Education

Bachelor's or Master's in CS/AI/DS

Tools

FastAPI

Pydantic

Docker

AWS

PostgreSQL

S3

Bedrock Titan

Job description

Build and ship production agentic AI features — agents, tools, prompts, evals, and integrations — against an established reference architecture.

Required Qualifications:

• 3–8 years of experience in software development or data engineering
• Hands‑on experience in Generative AI or LLM‑based applications
• Experience building APIs, microservices, or distributed systems
• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field

Key roles:

• Implement agents and sub‑agents (planner, executor, critic, router) using Claude Agent SDK / Lang Graph
• Build tools and MCP integrations, design clean tool schemas, idempotent operations, and robust error handling.
• Implement RAG pipelines: ingestion, chunking, embedding (Bedrock Titan), hybrid retrieval, citation rendering.
• Develop Fast API/Python services exposing agent capabilities (sync + streaming); integrate with SQL (Postgres) and object stores (S3).
• Write evaluation harnesses (golden sets, regression suites, LLM‑as‑judge) and trace/observe agent runs.
• Implement guardrails: input/output validation, schema enforcement, rate limiting, prompt‑injection defenses.
• Participate in code reviews, pairing, and architecture discussions; own quality of the code you ship.
• Strong Python (FastAPI, async, Pydantic) or Node/TypeScript equivalent.
• Hands‑on with at least one agent framework (Claude Agent SDK / Lang Graph / AutoGen).
• Practical experience with LLM tool/function calling, structured outputs, streaming.
• RAG implementation experience (pgvector / FAISS / OpenSearch).
• Git, CI/CD, containerization (Docker), and cloud basics (AWS preferred).

Roles/Responsibilities:

• Implement single-agent and multi-agent systems using frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similar
• Build applications using LLMs (Azure OpenAI, OpenAI, Anthropic, etc.)
• Implement Retrieval-Augmented Generation (RAG) pipelines
• Enable agents to coordinate and collaborate in multi-agent ecosystems
• Build secure, scalable APIs and microservices to support AI agents
• Develop evaluation frameworks for agent performance (accuracy, hallucination detection, response quality)
• Monitor system behavior and continuously improve reliability
• Optimize performance for latency, cost, and scalability