Location: Kolkata, India Reports To: CTO About Cozeva Cozeva is a leading population health and value-based care platform that empowers payers, providers, and patients with data-driven insights. We are building the next generation of healthcare intelligence — integrating AI/ML, FHIR interoperability, and digital quality standards — to transform healthcare delivery and equity. Role Overview We are seeking a Senior Applied ML Engineer to bridge the gap between research and production. This role is for someone who understands both machine learning models and enterprise-scale software systems — ensuring Cozeva’s AI innovations are deployed, monitored, and scaled reliably within our SaaS platform. You will work at the intersection of data science, AI, and software engineering, helping turn prototypes into high-performing, production-grade AI features that power value-based care. Key Responsibilities ● Model Integration & Deployment ○ Embed ML/AI models (LLMs, NLP, risk models, forecasting) into Cozeva’s software workflows and APIs. ○ Build production pipelines for training, evaluation, and deployment on cloud-native infrastructure. ● Scalable Systems Engineering ○ Design and maintain distributed data pipelines for claims, clinical, and EHR data. ○ Ensure performance, reliability, and cost efficiency of AI workloads (Aurora MySQL, Redshift, S3, EC2/K8s). ● MLOps Practices ○ Implement CI/CD for ML (model versioning, automated retraining, monitoring, rollback). ○ Monitor live model performance, drift, and fairness, ensuring compliance with Cozeva’s AI Governance Framework v1.1. ● Applied Problem Solving ○ Partner with AI Scientists to productionize models for: ■ NLP abstraction of clinical data (EHR notes, CCDs, FHIR). ■ Risk stratification and hospitalization/ED prediction. ■ Member engagement and HCC suspect modeling. ● Collaboration ○ Work closely with data engineers, SDEs, and product teams to deliver AI-driven features in Cozeva’s SaaS stack. Minimum Qualifications ● Bachelor’s or Master’s in Computer Science, AI/ML, or related field. ● 5–8 years of experience in building and deploying ML models into production. ● Strong software engineering skills: Python, SQL, Docker/Kubernetes, distributed systems. ● Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face) and MLOps tools (MLflow, Kubeflow, SageMaker, or equivalent). ● Deep understanding of cloud infrastructure (AWS preferred: S3, EC2, RDS/Aurora, Redshift, IAM). ● Track record of delivering production-ready ML/AI features at scale. Preferred Qualifications ● Experience with healthcare data (claims, EHR, FHIR, CCDs). ● Knowledge of value-based care, risk adjustment, or quality measurement. ● Experience designing responsible AI systems (bias detection, explainability, auditing). ● Contributions to open-source ML/MLOps projects. Why Join Cozeva? ● Mission with Impact: Improve health equity and outcomes for millions of patients. ● Hands-On AI: Deploy frontier models (LLMs, NLP, forecasting) directly into a SaaS platform. ● High Visibility: Collaborate closely with the CTO, scientists, and engineering leaders. ● Global Collaboration: Work with top-tier AI talent across the US and India. ● Culture of Service: Rooted in transparency, collaboration, and impact.
As a Senior Applied ML Engineer at Cozeva, you will play a crucial role in bridging the gap between research and production, ensuring that Cozeva's AI innovations are successfully deployed, monitored, and scaled within the SaaS platform. Your expertise in machine learning models and enterprise-scale software systems will be instrumental in turning prototypes into high-performing, production-grade AI features that drive value-based care. Key Responsibilities: - Model Integration & Deployment: - Embed various ML/AI models (LLMs, NLP, risk models, forecasting) into Cozeva's software workflows and APIs. - Build and maintain production pipelines for training, evaluation, and deployment on cloud-native infrastructure. - Scalable Systems Engineering: - Design and manage distributed data pipelines for claims, clinical, and EHR data. - Ensure the performance, reliability, and cost efficiency of AI workloads on Aurora MySQL, Redshift, S3, EC2/K8s. - MLOps Practices: - Implement CI/CD for ML, including model versioning, automated retraining, monitoring, and rollback. - Monitor live model performance, drift, and fairness to comply with Cozeva's AI Governance Framework v1.1. - Applied Problem Solving: - Collaborate with AI Scientists to deploy models for NLP abstraction of clinical data, risk stratification, hospitalization/ED prediction, and member engagement. - Collaboration: - Work closely with data engineers, SDEs, and product teams to deliver AI-driven features in Cozeva's SaaS stack. Qualifications Required: - Bachelors or Masters in Computer Science, AI/ML, or related field. - 5-8 years of experience in building and deploying ML models into production. - Strong software engineering skills in Python, SQL, Docker/Kubernetes, and distributed systems. - Experience with ML frameworks such as PyTorch, TensorFlow, Hugging Face, and MLOps tools like MLflow, Kubeflow, SageMaker, or equivalent. - Proficiency in cloud infrastructure, preferably AWS (S3, EC2, RDS/Aurora, Redshift, IAM). - Proven track record of delivering production-ready ML/AI features at scale. Why Join Cozeva By joining Cozeva, you will have the opportunity to: - Make a meaningful impact on health equity and outcomes for millions of patients. - Deploy cutting-edge models directly into a SaaS platform. - Collaborate closely with the CTO, scientists, and engineering leaders. - Work with top-tier AI talent globally in a culture of transparency, collaboration, and impact.,