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David Careers

Lead Software Engineer

Posted yesterday

x4 life sciencesHouston (TX)
Software DevelopersComputer Systems Design Services

SENIORITY

Lead

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About the role

Lead AI/ML Engineer | Healthcare AIX4 are partnered with a growing healthcare AI company that builds intelligent software for healthcare organisations. Their products use machine learning, generative AI and AI agents to cut through complex clinical and administrative work, so teams can make faster, better-informed decisions about patient care. Explainability, security and human oversight are built into how they work, and as they grow, they’re investing heavily in their AI engineering team. They’re now looking for a Lead AI/ML Engineer with a focus on production reliability, model evaluation and LLM observability. You’ll build and deliver production AI/ML solutions, lead a small team and own the evaluation and MLOps practices that keep AI systems reliable at scale. Much of the work supports a US government healthcare agency, so the bar for quality, security and explainability is high. You’ll work closely with engineering, product and agency stakeholders and have real influence over how the team builds and ships AI.
What you’ll be doing: AI/ML solution development. Build production AI/ML applications, develop ML models and generative AI solutions, and deliver APIs and AI services end to end. You’ll bring depth across the full stack, strong backend architecture, system design, scalability and reliability, with some frontend capability. Agentic AI and automation. Implement AI agents and workflow automation, design event-based architectures for high-volume document processing pipelines, triggering, orchestration, summarisation, structured data extraction and database persistence, and integrate those systems with enterprise platforms. Put clear agent controls and evaluation in place. LLM evaluation and production reliability. Own LLM output evaluation: hallucination reduction, accuracy measurement, and approaches such as LLM-as-judge or multi-LLM consensus. Apply agent regression testing, prompt benchmarking and red-teaming. Keep production systems healthy through observability, drift detection, versioning and rollback. MLOps and deployment. Deploy AI solutions, implement CI/CD and monitoring, and support production releases. Define requirements around throughput, SLAs, traffic patterns and scalability, whether workloads arrive in batches or continuously, before committing to an approach. Team and technical leadership. Lead an AI engineering team, mentor junior engineers, and conduct architecture and code reviews.
What you’ll need: 5+ years of relevant software engineering or AI/ML experience. Deep full-stack engineering experience: strong backend architecture, system design, scalability and reliability, with some frontend capability. Meaningful hands-on experience with AI/LLM systems, agentic AI, LLM-driven applications, document processing pipelines and production-grade AI solutions are all highly relevant. Candidates should demonstrate depth through the systems they have designed. The ability to design event-based architectures for high-volume, real-time or batch document processing: triggering, orchestration, summarisation, structured extraction and persistence. Strong problem-solving approach: able to clarify requirements around throughput, SLAs, traffic patterns and scalability before building. Solid understanding of LLM evaluation: hallucination reduction, accuracy measurement, and patterns such as LLM-as-judge or multi-LLM consensus. Strong Python, plus experience across AI/ML, cloud platforms, APIs and team leadership. Experience with MS SQL Server, including writing queries and designing schemas. A bachelor’s or master’s degree in a related field. US citizenship.
Nice to have:
  • Experience working in healthcare
  • Experience with LLM evaluation frameworks, agent regression testing, prompt benchmarking, red-teaming and production LLM observability.
  • Experience working with government agencies or in healthcare.
  • An active security clearance.
Tools: PyTorch, TensorFlow and scikit-learn; LangGraph and LangChain; OpenAI, Azure OpenAI, Anthropic and Gemini models. Vector databases: Pinecone, OpenSearch, Elasticsearch, pgvector or Milvus. Cloud and MLOps: AWS, Azure or GCP; Docker, Kubernetes, MLflow and Kubeflow. JavaScript or Node.js. What’s on offer Competitive salary Healthcare 401(k) BonusRemote, with occasional in-person attendance If you're interested, please apply, and reach out to

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