IFS
Data Scientist, Generative AI - IFS Copperleaf
IFS, San Francisco, California, United States, 94199
Job Description
The AI Core team at IFS Copperleaf is driving our AI-first strategy by building and scaling production-grade generative AI services that power enterprise SaaS solutions. As the Lead Generative AI Engineer, you will set technical direction, guide the development of domain-specific generative models, and lead engineering efforts across fine-tuning, evaluation, and deployment. You’ll work closely with engineering, product, and research stakeholders to deliver highly impactful AI capabilities. This role is approximately 70% hands-on model development and infrastructure leadership, and 30% technical strategy and cross-functional collaboration. What You’ll Do
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. Collaborate cross-functionally with product managers, designers, and domain experts to ensure successful delivery of impactful, AI-powered solutions. Qualifications
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. Excellent communication skills and a track record of technical leadership. A pragmatic and outcome-driven approach to AI innovation in complex domains. Additional Information
What We’re Offering
Salary Range: $250,000 - $275,000 plus bonus Flexible paid time off, including sick and holiday Flexible spending accounts Life insurance and disability benefits Tuition assistance Community involvement and volunteering events All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
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The AI Core team at IFS Copperleaf is driving our AI-first strategy by building and scaling production-grade generative AI services that power enterprise SaaS solutions. As the Lead Generative AI Engineer, you will set technical direction, guide the development of domain-specific generative models, and lead engineering efforts across fine-tuning, evaluation, and deployment. You’ll work closely with engineering, product, and research stakeholders to deliver highly impactful AI capabilities. This role is approximately 70% hands-on model development and infrastructure leadership, and 30% technical strategy and cross-functional collaboration. What You’ll Do
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. Collaborate cross-functionally with product managers, designers, and domain experts to ensure successful delivery of impactful, AI-powered solutions. Qualifications
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. Excellent communication skills and a track record of technical leadership. A pragmatic and outcome-driven approach to AI innovation in complex domains. Additional Information
What We’re Offering
Salary Range: $250,000 - $275,000 plus bonus Flexible paid time off, including sick and holiday Flexible spending accounts Life insurance and disability benefits Tuition assistance Community involvement and volunteering events All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
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