Artificial Intelligence Engineer

About AARC Environmental

AARC Environmental Inc. delivers market-leading EHS compliance solutions. We’re embedding AI into our core products leveraging RAG, LangChain, and fine-tuned LLMs to automate regulatory tracking, risk assessment, and client reporting.

Job Overview

and

Responsibilities

Build Agentic AI Systems

• Design autonomous and human-in-the-loop AI agents that can plan, reason, and execute multi-step tasks (task decomposition, routing, reflection, retry/rollback).
• Implement

  tool-use/function-calling

  for agents (Power BI, Monday.com, JotForm, FileMaker Data API, SharePoint/OneDrive, Azure Functions, email/Teams, SQL/KQL endpoints).
• Develop

  multi-agent patterns

  (planner/solver/critic, researcher/writer/reviewer) using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom state machines.
• Add

  guardrails & policies

  (PII masking, allowlists, rate-limits, cost/latency budgets, escalation triggers).

Architect End-to-End RAG Pipelines

• Design vector-database architectures (e.g., FAISS) for sub-second retrieval over PDFs, permits, SOPs, inspection reports, and FileMaker exports.
• Build ingestion pipelines (chunking, embeddings, hybrid search, citations/grounding) and relevance feedback loops.

Lead LLM Fine-Tuning & Optimization

• Apply parameter-efficient finetuning methods (QLoRA, PEFT, adapter layers) to base models (e.g., LLaMA, Mistral, Claude) for EHS tasks (reg-summary, defect classification, corrective-action drafting).
• Prompt-engineering + tool-use orchestration with LangChain / custom routers; maintain prompt registries and evals.

Productionize, Observe, and Improve

• Ship agents/models behind secure APIs; implement versioning, canary, A/B, and automated regression tests.
• Set up

  agent telemetry

  (traces, spans, tool-latency, token/cost), drift/outlier alerts, and

  safety rails

  .
• Define

  eval suites

  (task success, factuality/grounding, latency, cost, user-satisfaction) and drive continuous improvement.

Collaborate & Evangelize

• Partner with BI engineers, data architects, and compliance experts to translate requirements into robust AI solutions.
• Document patterns and standards best practices for RAG, vector databases, and finetuning workflows.
• Lead knowledge-sharing sessions

Qualifications

• Bachelor’s or Master’s in Computer Science, AI/ML, or related field
• 5+ years in AI/ML engineering with 2+ years focused on RAG architectures and production AI agents.
• Strong Python proficiency and deep familiarity with PyTorch or TensorFlow; proficiency with LangChain or equivalent orchestration frameworks.
• Hands-on experience with vector databases (e.g., FAISS), LangChain, and RAG pipelines
• Proven use of

  function-calling/tool-use

  and API integrations (Monday.com, JotForm, FileMaker Data API, SharePoint/Graph, Power BI REST, Azure Functions).
• Proven track record of finetuning LLMs using QLoRA/PEFT/LoRA
• Hands-on with vector databases (FAISS/pgvector/milvus) and doc processing at scale (PDF parsing/OCR).
• Familiarity with automated testing and version control for model endpoints
• Clear communicator who can present complex AI concepts to non-technical stakeholders
• Familiarity with dashboarding platforms (Power BI)
• Exposure to RESTful API integrations (JotForm API, Monday.com)