Lead AI Software Engineer

Streamline Healthcare Solutions

1d•$150,000 - $200,000•Remote

About The Position

This senior-level role focuses on designing and delivering AI/ML solutions for the healthcare industry—spanning LLM-powered applications, retrieval-augmented generation (RAG), and predictive models. You will co-design AI solutions with the AI Architect and own product-level design and end-to-end implementation (data pipelines, training/fine-tuning, evaluation & guardrails, deployment, and monitoring) within enterprise standards. The role is Azure-first and requires hands-on experience with OpenAI or Anthropic models, Microsoft Copilot, GitHub Copilot, and strong competence with SQL Server (SSMS) and Visual Studio. A commitment to HIPAA-compliant handling of PHI, Responsible AI, and measurable clinical and business outcomes is essential. Healthcare domain experience—either clinical or revenue cycle management—is required. This position is remote and based in the United States. The salary range is $150,000 - $200,000, DOE. Employment visa sponsorship is not available for this role.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Computer Information Systems, Health Informatics, or a related field.
Healthcare domain experience: either clinical (e.g., care delivery, clinical documentation, quality) or revenue cycle management (e.g., coding, claims, denials, prior auth).
10+ years in software engineering; 5+ years building and shipping ML/AI solutions; 2+ years leading AI/ML initiatives or teams.
Azure AI Foundry (Azure AI Studio) knowledge for developing, evaluating, and operationalizing LLM solutions; familiarity with Azure AI resources and deployment patterns.
Proficiency with SQL Server Management Studio (SSMS) for SQL development, performance tuning, and troubleshooting; strong T-SQL fundamentals.
Proficiency with Visual Studio and experience integrating AI services into .NET/C# applications or services where needed.
Hands-on experience using OpenAI or Anthropic models (e.g., GPT-4.x/4o, Claude 3.x), including prompt engineering, function/tool calling, and evaluation.
Experience with Microsoft Copilot and GitHub Copilot in professional workflows (coding assistance, test generation, documentation) with awareness of usage policies and data boundaries.
Strong Python and ML ecosystem: PyTorch/TensorFlow, transformers/Hugging Face, embeddings, LLM orchestration (e.g., LangChain or LlamaIndex), and vector databases (e.g., FAISS, Azure AI Search, Pinecone).
MLOps: MLflow/W&B (or equivalent), Docker, Kubernetes, CI/CD for ML, model registries, monitoring (drift, performance, bias), A/B testing, and rollback strategies.
Cloud AI on Azure (preferred): Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML, Azure Key Vault, with solid understanding of IAM, secrets, and encryption.
Demonstrated security, privacy, and compliance competence: HIPAA, PHI/PII handling and de-identification (e.g., Presidio), Responsible AI practices.
Excellent communication and cross-functional collaboration, including with clinical stakeholders and compliance teams.

Nice To Haves

Experience with FHIR and HL7 data standards; clinical NLP (entity extraction, summarization, coding/RCM use cases); and/or medical imaging (DICOM).
Databricks (Delta Lake, Spark), Airflow (or similar orchestration), and Azure-native data services (e.g., Data Factory, Synapse).
GPU/CUDA experience; inference optimization (quantization, distillation); prompt/token budgeting and caching strategies for LLM workloads.
Governance & ethics: model cards, datasheets for datasets, bias/fairness evaluations, and red-teaming.
Familiarity with .NET microservices and API design to integrate AI services into enterprise systems.

Responsibilities

Co-design AI solutions with the AI Architect and own product-level solutioning and delivery within enterprise AI architecture, standards, and governance.
Lead end-to-end implementation for your product/squad: RAG over EHR/claims/clinical text using embeddings and vector search.
Model development and training/fine-tuning (LLMs and classical ML), evaluation frameworks, and guardrails (hallucination reduction, safety, PII/PHI handling).
Production deployment with containerization/orchestration, and GPU-aware inference where applicable.
Establish and operate MLOps: experiment tracking and model registry, CI/CD for ML, canary/A/B testing, and monitoring for latency, accuracy, drift, bias, and cost.
Own reliability, security, and cost for your product’s AI services: define SLOs, participate in on-call/incident response, manage token/GPU budgets, and optimize prompts, embeddings, caching, and indexing.
Build and maintain data pipelines (e.g., Spark/Databricks or equivalent) and ensure robust SQL Server performance and data quality; collaborate with DBAs and data engineers using SSMS.
Ensure HIPAA compliance and Responsible AI practices across development and operations; partner with security and compliance to meet policy requirements.
Collaborate with product management, domain experts, and compliance to translate requirements into safe, reliable, high-impact AI services.
Conduct reviews emphasizing code quality, experiment rigor, reproducibility, and evaluation discipline; mentor engineers and data scientists.
Participate in architecture reviews; propose improvements and contribute reusable components (RAG templates, evaluation harnesses) back to the shared AI platform.
Leverage Microsoft Copilot and GitHub Copilot to improve developer productivity, code quality, and documentation, aligning with organizational governance.