AI/ML Engineer

Position Summary:

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.

The ideal candidate will have practical experience developing ML models, tuning and improving model performance, troubleshooting issues, and mentoring other team members who are building AI/ML solutions. This individual will also help establish best practices, reusable patterns, governance controls, and operational processes for enterprise AI and ML delivery.

This role is especially important for a healthcare organization where data quality, privacy, governance, explainability, compliance, and production reliability are critical.

Key Responsibilities

Machine Learning and Data Science

Design, build, train, tune, validate, and deploy machine learning models.

Support use cases such as prediction, classification, forecasting, anomaly detection, natural language processing, document intelligence, member/provider analytics, operational optimization, and risk identification.

Perform exploratory data analysis, feature engineering, model selection, model 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 business stakeholders to ensure models are built on trusted and governed data.

AI/ML Mentorship and Technical Leadership

Mentor and guide team members who are 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 to teams implementing AI and ML use cases.

MLOps Enablement

Define and implement MLOps practices across the machine learning lifecycle.

Support experiment tracking, model versioning, model registry, automated testing, CI/CD, deployment automation, and model monitoring.

Establish processes for model promotion from development to test to production.

Define model monitoring approaches for accuracy, drift, bias, performance, usage, and operational health.

Support retraining strategies, rollback procedures, alerting, incident response, and production support models.

Partner with platform, DevOps, data engineering, security, and governance teams to operationalize AI/ML solutions safely and reliably.

AI Solutions in Data and Healthcare Analytics

Help identify, assess, and design AI use cases across data, 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 model.

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.

Governance, Compliance and Production Readiness

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.

Required Skills:

• 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, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
• Practical experience with MLOps concepts such as model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, and retraining.
• Experience working with enterprise data platforms, cloud platforms, and modern data engineering practices.
• Strong understanding of data quality, feature engineering, model validation, and production support.
• Ability to translate business problems into AI/ML solution designs.
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.

Technical Skills

• Programming: Python, SQL
• Machine Learning: scikit-learn, XGBoost, TensorFlow, PyTorch, statistical modeling, forecasting, NLP
• MLOps: MLflow, Azure ML, Dataiku, model registry, CI/CD, GitHub Actions
• Data Platforms: Snowflake, Azure SQL, Oracle, data lakes, cloud data platforms
• AI/GenAI: LLMs, prompt engineering, RAG, semantic search, text-to-SQL, document intelligence
• Governance: model documentation, lineage, metadata, data quality, responsible AI, privacy and security controls

Desired Skills:

• Experience in healthcare, dental insurance, health insurance, financial services, or another regulated industry.
• Experience with platforms such as Azure ML, Dataiku, Databricks, Snowflake, MLflow, GitHub, GitHub Actions, Power BI, or similar tools.
• Experience with GenAI and LLM-based solutions.
• Experience designing AI solutions using enterprise data platforms such as Snowflake or cloud-based data ecosystems.
• Experience with responsible AI, model governance, bias detection, explainability, and audit requirements.
• Experience supporting AI governance councils, architecture reviews, or model risk review processes.
• Experience with healthcare data domains such as members, providers, claims, benefits, eligibility, call center, clinical, dental, or operational data.