AI​/ML Engineer

Overview

We are looking for an experienced Expert AI/ML Engineer to support and advance our enterprise AI, machine learning, and data science capabilities within a healthcare environment. This role requires a strong hands‑on background in building machine learning models, supporting data science teams, enabling MLOps, and helping operationalize AI use cases from concept to production.

• Design, build, train, tune, validate, and deploy machine learning models for use cases such as prediction, classification, forecasting, anomaly detection, NLP, document intelligence, member/provider analytics, operational optimization, and risk identification.
• Perform exploratory data analysis, feature engineering, model selection, evaluation, and performance tuning.
• Review model outputs and recommend improvements for accuracy, precision, recall, stability, fairness, and explainability.
• Troubleshoot model performance issues, data quality issues, model drift, production failures, and inconsistent predictions.
• Partner with data engineers, data scientists, analytics teams, and stakeholders to ensure models are built on trusted and governed data.
• Mentor and guide team members building machine learning and AI models.
• Provide hands‑on support for model design, development, testing, validation, and deployment.
• Conduct technical reviews of model architecture, code, features, evaluation metrics, and production readiness.
• Establish reusable standards, templates, checklists, and best practices for AI/ML delivery.
• Help upskill internal teams on data science, ML engineering, MLOps, responsible AI, and AI solution design.
• Serve as a trusted advisor for AI/ML use cases.
• Define and implement MLOps practices across the ML lifecycle: experiment tracking, model registry, automated testing, CI/CD, deployment automation, and monitoring.
• Establish processes for model promotion from development to test to production.
• Define monitoring approaches for accuracy, drift, bias, performance, usage, and operational health.
• Support retraining strategies, rollback procedures, alerting, incident response, and production support.
• Partner with platform, Dev Ops, data engineering, security, and governance teams to operationalize AI/ML solutions safely and reliably.
• Help identify, assess, and design AI use cases in analytics, reporting, operations, governance, and automation.
• Provide technical guidance for AI solutions involving GenAI, LLMs, text‑to‑SQL, semantic search, summarization, document processing, NLP, and predictive analytics.
• Define end‑to‑end solution approaches, including data requirements, architecture, model strategy, governance controls, deployment approach, and support.
• Help move AI initiatives from proof of concept to production‑grade implementation.
• Ensure AI solutions are designed with healthcare data privacy, security, explainability, auditability, and responsible AI principles in mind.
• Support AI/ML governance processes, including model documentation, approval workflows, risk assessment, validation, and auditability.
• Ensure solutions follow enterprise standards for data security, privacy, access control, and regulatory expectations.
• Partner with security, compliance, legal, privacy, architecture, and data governance teams as needed.
• Define production‑readiness criteria for AI/ML solutions.
• Support responsible AI practices, including bias review, explainability, transparency, human‑in‑the‑loop controls, and monitoring.

• 8+ years of experience in machine learning, data science, AI engineering, ML engineering, or related roles.
• Strong hands‑on experience building, tuning, validating, and deploying ML models.
• Experience mentoring data scientists, ML engineers, data engineers, or analytics teams.
• Strong knowledge of supervised learning, unsupervised learning, classification, regression, forecasting, NLP, and model evaluation techniques.
• Experience with Python and common ML/data science libraries such as pandas, Num Py, scikit‑learn, XGBoost, Tensor Flow, PyTorch, or similar.
• Practical experience with MLOps concepts such as model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, and…