AI Engineer (SOP Automation & Document Generation) at Distributed Renewable Energy Enhancement Facility (DREEF)

Job Summary

DREEF is recruiting an AI Engineer to lead the design and deployment of generative AI solutions that automate documentation workflows, streamline multi-step review processes, and enhance the productivity of cross-functional project teams. This role will focus on integrating large language models (LLMs) into digital project management infrastructure to enable real-time document drafting, workflow compliance, and scalable delivery of high-quality outputs across multiple projects.

Job Details

Key Responsibilities

•  Design and implement AI-powered drafting templates for SOP outputs including Feasibility Reports, ESMS Manuals, GRMs, and Transaction Packs.
•  Translate SOP-compliant templates into prompt workflows using GPT or equivalent LLMs.
•  Integrate Microsoft Forms/Excel Online data collection with AI drafting engines via Power Automate or APIs.
•  Fine-tune LLMs or use RAG pipelines (e.g., LangChain + vector database) to improve project-specific drafting quality.
•  Collaborate with PMTs (Technical, ESG, Legal, Finance) to define and maintain logic rules for narrative drafting.
•  Develop internal review interfaces for human-in-the-loop validation and approval of AI-generated documents.
•  Support DREEF’s SSOT architecture by ensuring all AI-generated outputs are stored in version-controlled environments.
•  Monitor AI performance: drafting time reduction, approval rates, accuracy, and quality feedback.
•  Continuously update drafting templates and AI prompt libraries based on regulatory or donor feedback.
•  Stay current with advances in AI tooling (e.g., Azure OpenAI, Claude, Anthropic, Hugging Face) for secure deployment

Performance Indicators

•  Reduction in SOP document drafting time by 50%+ within 6 months.
•  80% first-pass acceptance rate of AI-generated outputs by reviewers.
•  Automation of 10+ core SOP outputs via GPT workflows.
•  End-to-end integration of at least 3 SOP drafting pipelines (e.g., Feasibility, ESMS, PUE).
•  Documentation of reusable prompt libraries and drafting templates for scale.

Strategic Role Context

• This role supports the implementation of a data-driven automation model in which structured inputs from subject-matter experts are assembled by AI into standardized document outputs, then validated by internal reviewers. The AI Engineer will work across teams to design and operationalize prompt workflows and reviewer integration logic within a phased digital execution model. Phase One focuses on form-based data collection, tracking, and dashboards using cloud productivity tools. Phase Two introduces LLMs (e.g., GPT) to automate the drafting of technical, financial, ESG, and contractual documents.

Additional Responsibilities 

•  Map the structured input and review workflow into modular AI drafting components.
•  Design prompt workflows to convert form-based submissions into first-draft outputs across various content types (technical, financial, ESG, legal).
•  Integrate AI review and approval logic into cloud-based version-controlled environments.
• Collaborate with internal stakeholders to ensure output alignment with pre-defined standards and compliance templates.
•  Refine and deploy prompt libraries tied to discrete workflow stages (Phase One: structured input; Phase Two: AI-assisted outputs).

Requirements

• Bachelor’s or Master’s in AI, Data Science, Machine Learning, Computer Science, or related field.
• 4+ years of experience in deploying ML or LLM-based solutions in production environments.
• Proficiency in Python and libraries such as LangChain, Hugging Face Transformers, PyTorch, and scikit-learn.
• Hands-on experience building GPT-powered document automation tools or prompt engineering frameworks.
• Experience with Microsoft ecosystem (Power Automate, Excel Online, Forms, SharePoint) is a strong advantage.
• Familiarity with vector databases (e.g., FAISS, Pinecone) and orchestration tools (e.g., Airflow, LangChain).
• Demonstrated ability to implement RAG pipelines and secure LLM integrations (e.g., Azure OpenAI).
• Experience integrating AI solutions into digital workflows or enterprise systems.
• Strong collaboration skills and experience working with interdisciplinary teams.