Senior ML Engineer

Summary

Join Qventus, a leading AI-powered healthcare solutions company, as a Senior Machine Learning Engineer with a strong ML Ops focus. You will build and scale AI solutions, working across the ML lifecycle from experimentation to deployment and monitoring. Collaborate with data scientists, engineers, and platform partners to design, deploy, and support the infrastructure behind production ML and LLM models. Lead efforts in packaging, monitoring, and scaling models across critical healthcare workflows. Contribute to experimentation and development, ensuring AI systems are reliable and reproducible for real-world impact. This role offers the opportunity to significantly improve healthcare quality and patient outcomes. The ideal candidate will have extensive experience in ML systems development and ML Ops infrastructure, along with strong collaboration and communication skills.

Requirements

• 3+ years of experience developing and maintaining machine learning systems in production environments (Python, SQL)
• 1+ years experience supporting ML Ops infrastructure (model packaging, orchestration, observability, CI/CD)
• Hands-on experience with model lifecycle tools such as MLflow, SageMaker, or similar
• Familiarity with operationalizing LLMs or retrieval-augmented generation (RAG) systems; Exposure to LLM frameworks and libraries (Langchain, LlamaIndex, HuggingFace, etc.)
• Strong understanding of software engineering principles and writing maintainable, modular code
• Practical experience with the following tools & services: AWS services (Lambda, S3, RDS, CloudWatch), Databricks, Spark, Terraform (Atmos)
• Strong collaboration and communication skills — able to partner closely with product, clinical, and engineering stakeholders

Responsibilities

• Build and support end-to-end ML pipelines for training, validation, deployment, and monitoring of traditional ML and LLM-based solutions
• Partner with data scientists to productionize models with a focus on reproducibility, observability, and runtime efficiency
• Manage ML infrastructure, including experiment tracking, model versioning, and deployment tooling
• Contribute to the design and implementation of scalable, low-latency model inference and batch prediction systems

Preferred Qualifications

• 3+ years applied or research experience using a wide variety of statistical and machine learning techniques - particularly in NLP, explainable ML (Python)
• Experience supporting cloud-based, highly available, observable, and scalable data platforms utilizing large, diverse data sets in production to meet ambiguous business needs
• Strong background in data quality validation and model monitoring in healthcare or regulated environments
• Ability to contribute to feature engineering or algorithm tuning in partnership with domain experts
• Prior experience working in healthcare, particularly with EMR, claims, or hospital operations data
• Master’s degree in Computer Science, Engineering, or related field, or equivalent industry experience

Benefits

• Open Paid Time Off
• Paid parental leave
• Professional development
• Wellness
• Technology stipends
• Generous employee referral bonus
• Employee stock option awards