Senior ML Engineer

ABOUT THIS POSITION

We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain‑specific AI systems using Language Models (LMs) and agentic architectures. As a core member of the team, you will be instrumental in developing the entire ML pipeline, from sophisticated data extraction techniques to fine‑tuning specialized LMs and orchestrating their interactions within a multi‑agent framework.

This is a unique opportunity to apply state‑of‑the‑art Generative AI and NLP techniques to a real‑world, high‑impact problem, leveraging the latest research in agentic AI and LMs to deliver economical and powerful solutions.

• Data Pipeline & Knowledge Base Construction
  • Design, implement, and optimize robust pipelines for ingesting, parsing, and extracting structured information from complex documents (leveraging OCR, document layout analysis, Named Entity Recognition (NER), and Relationship Extraction (RE)).
  • Develop rich, nested JSON schemas for representing structured data and ensure scalable storage.
  • Generate and manage high‑quality vector embeddings for efficient retrieval‑augmented generation (RAG) within a Vector Database.
• Language Model (LM) Development & Fine‑tuning
  • Research, select, and experiment with appropriate open‑source Language Models (Large & Small) (e.g., Phi‑3, Mistral, Llama, Nemotron‑H families) for specialized tasks.
  • Design and execute efficient fine‑tuning strategies (e.g., LoRA, QLoRA, full fine‑tuning) on curated, domain‑specific datasets to achieve precise performance for tasks like coverage determination, code lookups, and policy rule application.
  • Explore and implement knowledge distillation techniques to transfer capabilities from larger models to smaller, more efficient LMs.
• Agentic System Design & Implementation
  • Build and maintain the core agentic framework, including the orchestrator that intelligently routes queries and coordinates interactions between various specialized LM tools.
  • Develop and integrate "tools" (specialized LMs and external APIs) that perform atomic medical necessity tasks, ensuring strict behavioral alignment and structured outputs.
• MLOps & Deployment
  • Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run.
  • Implement robust MLOps practices for continuous integration, continuous delivery (CI/CD), model versioning, and performance monitoring (latency, throughput, accuracy).
• Continuous Improvement & Research
  • Establish effective feedback loops from end‑user interactions and system logs to identify areas for model improvement.
  • Curate and expand training datasets, ensuring data privacy (PHI/PII masking) and legal compliance.
  • Stay abreast of the latest research in LMs, agentic AI, NLP, and document understanding, applying relevant advancements to our system.
• Collaboration
  • Work closely with subject matter experts, product managers, and other engineers to translate complex requirements into technical solutions and evaluate system performance.

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
• 3+ years of professional experience in Machine Learning Engineering, with a strong focus on NLP.
• Proven experience with Language Models (LMs), including model selection, fine‑tuning, and deployment.
• Strong proficiency in Python and familiarity with ML frameworks (e.g., PyTorch, Tensor Flow, Hugging Face Transformers).
• Solid understanding and hands‑on experience with core NLP techniques and architectures, especially Transformers.
• Experience with cloud platforms, particularly Google Cloud Platform (GCP), including services like Vertex AI, Cloud Storage, and compute services.
• Familiarity with MLOps principles and tools for model serving, monitoring, and pipeline automation.
• Excellent problem‑solving skills, attention to detail, and ability to work independently and collaboratively.
• Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive innovation, and improve decision‑making across business functions.
• Abi…