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Artificial Intelligence Engineer

Artificial Intelligence Engineer

AARC Environmentalhyderabad, India
1 day ago
Job description

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)
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