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 We are seeking a hands-on AI Engineer with deep experience in Generative AI, agentic AI, and ML engineering. You will architect end-to-end RAG systems and build production-grade AI agents that plan tasks, call tools/APIs, and autonomously orchestrate EHS workflows (e.g., document intake, permit monitoring, data entry/QA, risk flagging). You’ll own model and agent design, evaluation, deployment, and monitoring in partnership with data engineers and compliance SMEs. 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)