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Senior AI​/ML Engineer

Job in Portland, Multnomah County, Oregon, 97204, USA
Listing for: Latitude
Full Time position
Listed on 2026-08-30
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, AWS, Data Scientist
Salary/Wage Range or Industry Benchmark: 140000 - 200000 USD Yearly USD 140000.00 200000.00 YEAR
Job Description & How to Apply Below

OUR IDEAL CANDIDATE

We are looking for a Senior AI/ML Engineer who wants to leverage cutting-edge Artificial Intelligence, including Generative AI, LLMs, and modern Agentic frameworks—to fix a broken healthcare system. The ideal candidate possesses a strong foundation in production machine learning, deep experience with cloud-native GenAI architectures (specifically on AWS), and a proven track record of working with sensitive healthcare data. You thrive in a high-impact, collaborative environment where you will design and deploy scalable AI-driven solutions that automate complex billing workflows, detect fraud, extract insights from unstructured claims, and power our next generation of AI products.

Responsibilities
  • Design, build, and orchestrate Machine Learning, GenAI, and Agentic AI applications leveraging foundational models (via AWS Bedrock, Sage Maker) for automated billing, cost prediction, and document intelligence.
  • Design and build production-grade scalable Machine Learning (ML) pipelines integrating Object Character Recognition (OCR), Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and agentic decision-making workflows to process complex medical claims and unstructured data.
  • Establish end-to-end MLOps standards, automated model testing, and robust CI/CD pipelines for automated build, test, and release cycles of both ML models and AI services.
  • Define, provision, and maintain secure cloud environments for AI/ML services using Infrastructure as Code (IaC) tools such as AWS CDK or Terraform.
  • Develop APIs and microservices to enable seamless integration of ML/AI models into existing business applications.
  • Collaborate with cross-functional teams (product, technology, operations, etc.) to align AI solutions with business goals.
  • Implement techniques to improve transparency in AI decision-making, ensuring traceability and model explainability.
  • Ensure compliance with healthcare data regulations (e.g., HIPAA, HITRUST, SOC 2, etc.).
Qualifications
  • Bachelor's or Master's (preferred) degree in Computer Science, Data Science, Engineering, or a related quantitative field.
  • 5+ years of hands-on experience building, deploying, and maintaining production ML, GenAI, and Agentic solutions enterprise-wide for business applications.
  • Deep practical knowledge of combining OCR engines (e.g., AWS Textract) with NLP, LLMs, VLMs, and AI foundational models to parse, structure, and query complex healthcare documents.
  • Proven expertise within the AWS ecosystem (AWS Bedrock, Sage Maker, Lambda, ECS/EKS) and proficiency in Python, PyTorch/Tensor Flow, and modern GenAI and Agentic frameworks (e.g., Strands Agents, Agent Core, Kiro, etc).
  • Strong foundation in algorithm design, data structures, containerization (Docker, Kubernetes), and ability to deploy ML models as scalable APIs using Flask, FastAPI, or similar frameworks.
  • Strong hands-on experience using Infrastructure as Code (IaC) tools like AWS CDK or Terraform to manage cloud infrastructure, paired with practical experience building and maintaining CI/CD pipelines (e.g., Git Hub Actions, AWS Code Pipeline, etc).
  • Experience working with healthcare data structures (e.g., insurance claims, EHRs, medical billing coding like CPT/ICD, or EDI 837/835 feeds).
  • Strong understanding of HIPAA compliance, data masking, secure prompt/data handling, and AI safety guardrails.
  • You are intellectually curious, obsessed with measurable business impact, and naturally inclined to build better solutions.
  • Strong problem-solving skills and the ability to work independently.
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Position Requirements
10+ Years work experience
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