2 months ago
Responsibilities
- Apply generative AI and LLM-based techniques to develop, scale, and integrate machine learning systems into large-scale healthcare SaaS environments.
- Design, build, evaluate, and deploy predictive models, summarization systems, semantic search solutions, and other healthcare AI solutions.
- Develop high-level solution architectures and collaborate with data engineers, decision science analysts, and cross-functional teams on model development and delivery.
- Manage data, experimentation, model evaluation, quality, fairness, safety, and responsible AI practices using tools and frameworks such as LLM-as-judge, RAGAS, and LangSmith.
- Lead client-facing architecture and troubleshooting discussions, translate technical insights into measurable business impact, and identify client opportunities.
- Lead and mentor technical teams, senior resources, and team leads while providing thought leadership and industry best practices.
- Coordinate with product, clinical, data science, engineering, and offshore delivery teams to ensure effective communication and project delivery.
Requirements
- Ph.D. graduate with at least 1 year of related industry experience or master’s graduate with at least 4 years of industry experience, including at least 1 year directly related to healthcare ML/AI.
- Multiple major product releases in a commercial SaaS environment and hands-on experience with healthcare data such as EHR, ADT, or clinical notes.
- Proficiency in Python and SQL, with Java or other programming languages helpful.
- Experience with data engineering, large datasets, and big-data frameworks such as Azure Data Lake, Apache Spark, or Databricks.
- Strong understanding of transformer models and LLM approaches, including prompt tuning, PEFT methods such as LoRA and QLoRA, and Hugging Face Transformers.
- Experience with NumPy, SciPy, Pandas, Scikit-learn, PyTorch, and LightGBM for model development and evaluation.
- Experience building and deploying production ML models on Azure, AWS, or Google Cloud at SaaS scale.
- Experience with reinforcement learning or RLHF, NLP for summarization, extraction, classification, or semantic search, RAG pipelines, or agentic frameworks such as LangChain or LlamaIndex.
- Ability to lead multiple large projects, mentor team members, manage stakeholders, communicate complex technical plans clearly, and apply statistical techniques and data-quality judgment.
- Experience with Agile, Scrum, or DevOps software development methodologies is desirable.
Benefits
- Remote work from the USA.
- Opportunities to work on cutting-edge AI, ML, data, and cloud technologies with Fortune 500 companies.
- Upskilling and professional growth in a research-focused organization with 60+ patents filed.
- Opportunity to contribute to complex challenges at a rapidly growing AI-first digital engineering company.
Tech Stack
Categories
About Quantiphi
Quantiphi is an AI-first digital engineering and consulting firm that designs and implements machine learning, data, and cloud solutions for large enterprises across industries. Its teams build production systems—such as generative AI applications, computer vision, and predictive analytics—primarily on Google Cloud and other hyperscalers, delivered as professional services and managed solutions. Founded in 2013 and headquartered in Marlborough, Massachusetts, the company is privately held and a Google Cloud partner.