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# AI Full Stack Engineer

- **Company:** Pinnacle Inc.
- **Location:** Mount Pleasant Township, PA, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Continuous Integration, Software Debugging, Fiddler (Software), Monitoring of Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Language Modeling, Node.Js, Software Product Management, Search Technologies, Software Engineering, Pinecone, Data Processing, Scripting, LangChain, ReactJS, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Backend, Agentic-AI, Pgvector, Few-Shot Learning, LangSmith, CrewAI, Weaviate, AutoGen, Machine Learning Operations, Claude, Functional Programming, Api Design, Api Gateway, GPT, Software Version Control, OpenSearch, Programming Languages
- **Published:** October 16, 2026
- **Apply:** https://www.juju.com/job/2_164767022

## About the Role

3+ years of professional software engineering experience, including hands-on experience building AI/LLM-powered applications.

Cloud & Infrastructure

 * AWS Bedrock - hands-on experience building and deploying models and agents on the Bedrock platform (foundation model access, Knowledge Bases, Agents for Bedrock).
 * Familiarity with broader AWS ecosystem (Lambda, S3, IAM, API Gateway, etc.).

Agent Development

 * Proven, hands-on experience building AI agents - including autonomous agents, tool-calling agents, ReAct / plan-and-execute patterns, and multi-agent orchestration.
 * Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or AWS Bedrock Agents.
 * Strong understanding of agent memory, context management, and tool use.

Languages & Frameworks

 * Python - proficient; used for ML pipelines, agent logic, data processing, and scripting.
 * Node.js - proficient; used for API development, backend services, and agent integration layers.

LLM / SLM Expertise

 * Hands-on experience working with Large Language Models (e.g., Claude, GPT-4, Llama, Mistral) and Small Language Models (e.g., Phi-3, Gemma, Mistral 7B).
 * Practical knowledge of prompt engineering, few-shot learning, RAG (Retrieval-Augmented Generation), and fine-tuning workflows.

Monitoring & Observability

 * Experience with Fiddler AI or comparable agent/model monitoring tools (e.g., LangSmith, Arize, Weights & Biases, Helicone).
 * Ability to define and track agent performance metrics: accuracy, latency, hallucination rate, tool-call success, and cost.

Preferred Qualifications

 * Experience with vector databases (Pinecone, pgvector, OpenSearch, Weaviate) for semantic search and RAG pipelines.
 * Familiarity with MLOps practices - CI/CD for models, model versioning, A/B evaluation.
 * Exposure to multi-modal models (vision + language).
 * Prior work in an AI product or platform team at scale.
 * AWS certifications (e.g., AWS Certified Machine Learning - Specialty) are a plus., * Hands-on builder - You write code, ship agents, and debug production issues - not just architect on whiteboards.

## Description

We are looking for a hands-on AI Full Stack Engineer to join our growing AI Center of Excellence. In this role, you will design, build, and operationalize intelligent AI agents and LLM-powered solutions that drive real business impact. You will work at the intersection of cloud infrastructure, modern AI frameworks, and production-grade software engineering - owning the full lifecycle from prototype to deployment., * Design and develop AI agents - including autonomous, multi-step, and tool-using agents - using AWS Bedrock and leading agentic frameworks.

 * Build, fine-tune, and integrate Large Language Models (LLMs) and Small Language Models (SLMs) into production workflows.
 * Develop backend services and agent orchestration layers using Node.js and Python.
 * Implement agent monitoring, observability, and evaluation pipelines using tools such as Fiddler AI and comparable platforms.
 * Collaborate with product and engineering teams to translate business requirements into robust AI-powered features.
 * Establish best practices for responsible AI, prompt engineering, model evaluation, and agent safety.
 * Continuously evaluate emerging models, frameworks, and tooling to keep the platform at the cutting edge.
 * Contribute to internal documentation, architecture reviews, and knowledge sharing across the team.