IFS Copperleaf is driving its AI-first strategy by building and scaling production-grade generative AI services to power enterprise SaaS solutions. The Lead Generative AI Engineer will set technical direction and guide the development of domain-specific generative models, fine-tuning, evaluation, and deployment to deliver impactful AI capabilities.
Requirements
- 7+ years of experience in ML/AI engineering or applied research roles, with at least 3+ years focused on LLMs or generative AI.
- Proven leadership in delivering LLM-based features to production—fine-tuning, prompt engineering, evaluation, and model deployment.
- Strong expertise in Python, PyTorch, Hugging Face Transformers and PEFT libraries, LangChain, LlamaIndex, and related GenAI frameworks.
- Hands-on experience with Azure OpenAI Services, Azure AI Search, Azure ML, and scalable model deployment on cloud infrastructure.
- Familiarity with vector stores (FAISS, Qdrant, Pinecone), graph databases, and retrieval architecture.
- Strong software engineering skills and comfort with MLOps best practices—CI/CD, containerization, observability, and versioning.
Responsibilities
- Lead the design, fine-tuning, and optimization of large language models (LLMs), applying techniques such as supervised fine-tuning (SFT), parameter-efficient tuning (LoRA/Q-LoRA), and reinforcement learning from human feedback (RLHF).
- Architect and oversee data pipelines for LLM training, including synthetic data generation, human-in-the-loop annotation, and domain-specific corpus curation.
- Design and implement robust evaluation frameworks to ensure model quality, mitigate hallucinations, and support continuous improvement.
- Lead the development of retrieval-augmented generation (RAG) systems using vector databases, graph stores, and structured data integrations to ground LLM responses in enterprise knowledge.
- Guide infrastructure choices for model training and inference, including orchestration frameworks, model serving tools (vLLM, Triton), and scalable Azure-based deployment strategies.
- Build and maintain a model catalog with documented capabilities, performance dashboards, and demos.
- Stay on the cutting edge of open-source and commercial GenAI tooling, proposing and piloting next-gen features aligned with business priorities.
Other
- Approximately 70 % hands-on model development and infrastructure leadership, and 30 % technical strategy and cross-functional collaboration.
- Collaborate cross-functionally with product managers, designers, and domain experts to ensure successful delivery of impactful, AI-powered solutions.
- Excellent communication skills and a track record of technical leadership.
- A pragmatic and outcome-driven approach to AI innovation in complex domains.
- Flexible paid time off, including sick and holiday
