N-Power Medicine
About N-Power Medicine
Position Overview
Role Objectives and Responsibilities
- Architect and spearhead the development of cutting-edge, scalable AI infrastructure, including novel human-in-the-loop (HITL) paradigms, ensuring our systems learn effectively from feedback.
- Lead the technical design and implementation of core MLOps components and systems for our LLMs—including CI/CD, monitoring, and automated feedback loops—ensuring robustness, scalability, and adherence to software engineering best practices.
- Define and shape solutions for complex automation and deployment challenges, enabling the strategic application of our cutting-edge AI.
- Drive technical alignment and integration with AI Data Science and Software Engineering teams, ensuring the seamless transition of AI solutions from research into production environments and influencing architectural standards.
- Define and establish standards for the rigorous validation, monitoring, and lifecycle management of AI products, ensuring continuous accuracy improvement and reliability in production.
- Define, champion, and drive adoption of best practices for MLOps, including model/data versioning, experiment tracking, and reproducibility within the AI/ML domain; actively mentor others.
- Identify, champion, and integrate state-of-the-art MLOps technologies and frameworks, driving innovation and maintaining our technical edge in AI deployment.
- Provide expert guidance on applying safeguards and protections (HIPAA, privacy laws) to our model deployment and data handling pipelines; champion and uphold the highest compliance, quality, and security standards.
- 3+ years of professional experience in an MLOps, DevOps, or Software Engineering role with a focus on machine learning systems.
- MSc/BSc graduate in engineering, computer science, or a relevant field, with extensive equivalent experience. A PhD is a plus.
- Deep, hands-on expertise in Python and proficiency in modern software development practices.
- Hands-on experience with a major cloud platform (AWS, GCP, or Azure).
- Strong experience with containerization and orchestration technologies (Docker, Kubernetes).
- Proven experience building and maintaining CI/CD pipelines for complex applications (e.g., GitHub Actions, Jenkins), particularly those that include data + model versioning.
- A proven track record of technical leadership and high-impact contributions in building and scaling production machine learning systems.
- Proven ability to independently define, architect, and lead solutions for complex, ambiguous infrastructure problems, clearly articulating business value.
- Demonstrated ability to lead the decomposition of large-scale systems and guide teams in delivering incremental solutions.
- Track record of designing sustainable, reusable, and high-quality code and influencing team/organizational standards.
- Exceptional written, verbal, and presentation skills; ability to influence stakeholders at all levels.
- Recognized technical leader, proactive, strategic thinker, and takes end-to-end ownership.
- Generous, Curious, and Humble.
Preferred Qualifications
- Direct experience productionizing Large Language Models (LLMs), including knowledge of prompting strategies, RAG, and fine-tuning.
- Deep expertise with the Databricks platform, including MLflow, Delta Tables, and Unity Catalog.
- Experience building data annotation and Human-in-the-Loop (HITL) systems from the ground up.
- Familiarity with vector databases (e.g., Pinecone, Chroma) and model serving frameworks (e.g., Ray Serve, Triton, and -Databricks/Mosaic).
- Experience working in a regulated environment, particularly with healthcare data (HIPAA).
Travel Requirements
Pay Information
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Originally posted on Himalayas
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