Artificial Intelligence Engineer

iT Resource Solutions.net,inc; Boston (MA); On-site; USD 150,000 - 230,000; Full time

Job summary

iT Resource Solutions.net,inc in Boston seeks an Agentic AI Engineer to design, build, and deploy production-grade AI systems that autonomously execute complex business workflows.

You will work on LLM-powered agents, RAG pipelines, memory and observability, and integration with enterprise APIs using Python, FastAPI, React/TypeScript, and modern MLOps on AWS.

Qualifications

• Production-grade AI systems design, deployment, and observability.

Responsibilities

• Design and architect production-grade Agentic AI systems capable of reasoning, planning, and executing business workflows.
• Build LLM-powered agents and multi-agent workflows using modern AI frameworks and orchestration patterns.
• Develop scalable RAG pipelines with document ingestion, embedding, vector search, retrieval, ranking, and contextual generation.
• Integrate LLMs with enterprise systems through backend APIs, tools, function calling, and event-driven architectures.
• Build AI applications using OpenAI and other foundation models, optimizing prompts and tool usage.
• Develop reliable AI systems with memory, evaluation, guardrails, and human-in-the-loop workflows.
• Architect and develop backend services using Python and FastAPI.
• Build full-stack AI products and internal platforms using React and TypeScript.
• Design event-driven and asynchronous workflows for autonomous agents at scale.
• Implement semantic and vector search with Pinecone and FAISS.
• Develop AI/ML systems for fraud detection, risk scoring, anomaly detection, compliance automation, and intelligent operations.
• Establish evaluation frameworks and monitoring for agent accuracy, latency, cost, and production performance.
• Deploy and operate AI applications on AWS, Docker, Kubernetes, and MLOps practices.
• Collaborate with product, engineering, data, and business teams to identify AI-driven automation opportunities.

Skills

Python

OpenAI / LLM APIs

LangChain

LangGraph

LangSmith

Vector databases and semantic search

Pinecone / FAISS

FastAPI

REST APIs

React

TypeScript

Redis

AWS

Docker

MLflow

AI/ML evaluation and observability

Agent orchestration and tool/function呼

Event-driven architectures andworkflow

Tools

Docker

Kubernetes

AWS

Job description

Job Description

We are seeking a highly skilled **Agentic AI Engineer / Applied AI Engineer** to design, build, and deploy production-grade AI systems that go beyond conversational experiences and execute complex business workflows autonomously.

The ideal candidate will have strong experience in **LLM-based agents, Retrieval-Augmented Generation (RAG), multi-agent systems, AI automation, backend engineering, and full-stack AI product development**. This role requires the ability to translate complex user intent into reliable, scalable, and observable automated actions.

Key Responsibilities

• Design and architect **production-grade Agentic AI systems** capable of reasoning, planning, decision-making, and executing business workflows.
• Build **LLM-powered agents and multi-agent workflows** using modern AI frameworks and orchestration patterns.
• Develop scalable **RAG pipelines**, including document ingestion, embedding, vector search, retrieval, ranking, and contextual generation.
• Integrate LLMs with enterprise systems through **backend APIs, tools, function calling, and event-driven architectures**.
• Build AI applications using **OpenAI and other foundation models**, optimizing prompts, agent behavior, context management, and tool usage.
• Develop reliable AI systems incorporating **memory, evaluation, observability, guardrails, security, and human-in-the-loop workflows**.
• Architect and develop backend services using **Python and FastAPI**.
• Build full-stack AI products and internal platforms using **React and TypeScript**.
• Design event-driven and asynchronous workflows capable of supporting autonomous AI agents at scale.
• Implement semantic and vector search solutions using technologies such as **Pinecone and FAISS**.
• Develop AI/ML systems for areas including **fraud detection, risk scoring, anomaly detection, compliance automation, and intelligent business operations**.
• Establish evaluation frameworks and monitoring systems to measure **agent accuracy, reliability, latency, cost, and production performance**.
• Deploy and operate AI applications using **AWS, Docker, Kubernetes, and modern MLOps practices**.
• Collaborate with product, engineering, data, and business teams to identify opportunities for **AI-driven automation and intelligent workflows**.

Required Technical Skills

• **Python**
• **OpenAI / LLM APIs**
• **LangChain**
• **LangGraph**
• **LangSmith**
• **Vector databases and semantic search**
• **Pinecone / FAISS**
• **FastAPI**
• **REST APIs and backend development**
• **React / TypeScript**
• **Redis**
• **AWS**
• **Docker**
• **MLflow**
• AI/ML evaluation and observability
• Agent orchestration and tool/function calling
• Event-driven architectures and workflow automation

Preferred Experience

• Experience building **Agentic AI platforms or autonomous AI workflows** in production.
• Experience with **financial services, fraud detection, risk management, compliance, or enterprise automation**.
• Strong understanding of **LLM reasoning, retrieval, memory, planning, tool use, and agent evaluation**.
• Experience taking AI products from **prototype/MVP through production deployment and ongoing optimization**.
• Experience building scalable, secure, and reliable **enterprise AI applications**.

Target Roles

This position is suited for professionals working in or transitioning toward:

• **Agentic AI Engineering**
• **AI Automation Engineering**

Ideal Candidate Profile

The ideal candidate combines **AI/ML expertise with strong software engineering and product development skills**. You should be comfortable moving from an AI architecture concept to a working production system, building the underlying APIs and infrastructure, integrating LLMs and retrieval systems, and implementing the monitoring and guardrails necessary to make autonomous AI reliable in real-world enterprise environments.