Job Description:

JOB DESCRIPTION
AI Innovation Engineer
ABOUT THE ROLE
We are seeking a talented and driven AI Innovation Engineer to join our dedicated Innovation Program. This is a
high-impact role at the intersection of cutting-edge artificial intelligence, domain knowledge, and rapid prototyping.
You will work on complex, real-world challenges—experimenting with the latest AI technologies to assess
feasibility, demonstrate value, and build proof-of-concept (POC) solutions that can shape the future of our
business.
KEY RESPONSIBILITIES
AI/ML Research & Experimentation
• Design and execute AI/ML experiments to evaluate the feasibility of complex business problems.
• Explore and benchmark state-of-the-art models across GenAI, LLMs, Vision LLMs, traditional ML, and
Agentic AI frameworks.
• Conduct structured POC development cycles with clearly defined hypotheses, evaluation metrics, and
outcomes.
Document Intelligence & Computer Vision
• Develop AI pipelines to extract structured data from complex engineering documents including GA
drawings, datasheets, and multi-revision documents.
• Apply computer vision and Vision-Language Models (VLMs) to interpret technical diagrams, symbols,
tables, and annotations.
• Build cross-document comparison and revision-diff capabilities using AI/ML techniques.
Generative AI & LLM Applications
• Design and implement Retrieval-Augmented Generation (RAG) architectures tailored to domain-specific
engineering knowledge bases.
• Build, fine-tune, and evaluate LLM-based solutions for information extraction, summarization, and Q&A
over technical documents.
• Develop prompt engineering strategies, evaluation frameworks, and guardrails for production-grade GenAI
use cases.
Agentic AI & Automation
• Architect and prototype multi-agent AI systems capable of reasoning over complex, multi-step engineering
workflows.
• Leverage agent orchestration frameworks (e.g., LangGraph) to automate data extraction and validation
pipelines.
Azure AI Platform & Cloud Engineering
• Leverage Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, Azure Document
Intelligence, and Azure Cognitive Services to build scalable AI solutions.
• Manage experiment tracking, model versioning, and deployment pipelines using Azure ML and MLflow.
• Ensure solutions are cloud-native, cost-efficient, and aligned with enterprise security standards.
Rapid Prototyping & Stakeholder Engagement
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TechnipEnergies | General | Anyone - No Protection
• Rapidly prototype end-to-end AI solutions with minimal dependencies, demonstrating tangible business
value in short iteration cycles.
• Present POC findings, limitations, and recommendations clearly to both technical and non-technical
stakeholders.
• Document methodologies, architecture decisions, and results to build a reusable innovation knowledge
base.
REQUIRED SKILLS & TECHNOLOGIES
GenAI / LLMs
Azure AI Foundry
RAG Architecture
Vision LLMs (VLMs)
Agentic AI
Computer Vision
Azure OpenAI
Python
LangChain / LangGraph
Azure ML
Prompt Engineering
MLflow
Semantic Kernel
Document Intelligence
AutoGen
QUALIFICATIONS & EXPERIENCE
Traditional ML
• 3–5 years of hands-on experience in AI/ML engineering, with a portfolio of delivered POCs or production AI
systems.
• Proficiency in Python and key ML/AI libraries: PyTorch, Hugging Face Transformers, OpenCV, scikit-learn.
• Deep practical knowledge of Large Language Models (LLMs) and GenAI application development.
• Proven experience designing RAG pipelines, vector databases (e.g., FAISS, Azure AI Search, Chroma),
and embedding strategies.
• Hands-on experience with Vision LLMs and multimodal models (e.g., GPT-4o, LLaVA, Phi-3 Vision) applied
to document or image understanding.
• Strong working knowledge of the Azure AI ecosystem—Azure OpenAI, Azure ML, Azure AI Foundry,
Document Intelligence, Cognitive Services.
• Experience with Agentic AI frameworks such as LangGraph, AutoGen, or Semantic Kernel.
• Familiarity with traditional ML techniques (regression, classification, clustering, anomaly detection) and
when to apply them.
• Experience working with complex technical documents (engineering drawings, P&IDs, datasheets) is highly
desirable.
• Strong ability to rapidly prototype, iterate, and communicate results within tight timelines.
NICE TO HAVE
• Experience in the engineering, EPC, oil & gas, or industrial domain.
• Familiarity with OCR pipelines and layout-aware document models (e.g., LayoutLM, DocTR, Azure Form
Recognizer).
• Knowledge of graph-based AI or knowledge graph construction from unstructured engineering documents.
• Experience with MLOps practices: CI/CD for ML, model monitoring, and responsible AI frameworks.
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