Expert AI/ML Engineer

• s.Support use cases such as prediction, classification, forecasting, anomaly detection, natural language processing, document intelligence, member/provider analytics, operational optimization, and risk identificatio
• n.Perform exploratory data analysis, feature engineering, model selection, model evaluation, and performance tunin
• g.Review model outputs and recommend improvements for accuracy, precision, recall, stability, fairness, and explainabilit
• y.Troubleshoot model performance issues, data quality issues, model drift, production failures, and inconsistent prediction
• s.Partner with data engineers, data scientists, analytics teams, and business stakeholders to ensure models are built on trusted and governed dat

a. AI/ML Mentorship and Technical Leaders

• hipMentor and guide team members who are building machine learning and AI mode
• ls.Provide hands-on support for model design, development, testing, validation, and deployme
• nt.Conduct technical reviews of model architecture, code, features, evaluation metrics, and production-readine
• ss.Establish reusable standards, templates, checklists, and best practices for AI/ML delive
• ry.Help upskill internal teams on data science, ML engineering, MLOps, responsible AI, and AI solution desi
• gn.Serve as a trusted advisor to teams implementing AI and ML use cas

es. MLOps Enable

• mentDefine and implement MLOps practices across the machine learning lifecy
• cle.Support experiment tracking, model versioning, model registry, automated testing, CI/CD, deployment automation, and model monitor
• ing.Establish processes for model promotion from development to test to product
• ion.Define model monitoring approaches for accuracy, drift, bias, performance, usage, and operational hea
• lth.Support retraining strategies, rollback procedures, alerting, incident response, and production support mod
• els.Partner with platform, DevOps, data engineering, security, and governance teams to operationalize AI/ML solutions safely and relia

bly. AI Solutions in Data and Healthcare Anal

• yticsHelp identify, assess, and design AI use cases across data, analytics, reporting, operations, governance, and automa
• tion.Provide technical guidance for AI solutions involving GenAI, LLMs, text-to-SQL, semantic search, summarization, document processing, NLP, and predictive analy
• tics.Define end-to-end solution approaches, including data requirements, architecture, model strategy, governance controls, deployment approach, and support m
• odel.Help move AI initiatives from proof of concept to production-grade implementa
• tion.Ensure AI solutions are designed with healthcare data privacy, security, explainability, auditability, and responsible AI principles in

mind. Governance, Compliance and Production Rea

• dinessSupport AI/ML governance processes including model documentation, approval workflows, risk assessment, validation, and auditab
• ility.Ensure solutions follow enterprise standards for data security, privacy, access control, and regulatory expecta
• tions.Partner with security, compliance, legal, privacy, architecture, and data governance teams as n
• eeded.Define production-readiness criteria for AI/ML solu
• tions.Support responsible AI practices including bias review, explainability, transparency, human-in-the-loop controls, and monit, * thon, SQLMachine Learning: scikit-learn, XGBoost, TensorFlow, PyTorch, statistical modeling, forecas
• ting, NLPMLOps: MLflow, Azure ML, Dataiku, model registry, CI/CD, GitHu
• b ActionsData Platforms: Snowflake, Azure SQL, Oracle, data lakes, cloud data
• platformsAI/GenAI: LLMs, prompt engineering, RAG, semantic search, text-to-SQL, document int
• elligenceGovernance: model documentation, lineage, metadata, data quality, responsible AI, privacy and security

controls Key Succes

s MeasuresThe person in this role will be successful if

• they can:Help teams build better, more reliable
• ML models.Improve internal AI/ML development standards and best
• practices.Enable repeatable MLOps processes for production d
• eployment.Mentor team members and raise the organization's AI/ML
• maturity.Help convert AI ideas and POCs into production-ready
• solutions.Ensure AI/ML solutions are secure, governed, explainable, and su
• pportable.Support healthcare-grade operationalization with strong attention to privacy, compliance, and re

yWe 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, * cations8+ 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 tech
• niques.Experience with Python and common ML/data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or s
• imilar.Practical experience with MLOps concepts such as model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, and retr
• aining.Experience working with enterprise data platforms, cloud platforms, and modern data engineering pra
• ctices.Strong understanding of data quality, feature engineering, model validation, and production s
• upport.Ability to translate business problems into AI/ML solution d
• esigns.Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeh, * icationsExperience in healthcare, dental insurance, health insurance, financial services, or another regulated i
• ndustry.Experience with platforms such as Azure ML, Dataiku, Databricks, Snowflake, MLflow, GitHub, GitHub Actions, Power BI, or simila
• r tools.Experience with GenAI and LLM-based so
• lutions.Experience designing AI solutions using enterprise data platforms such as Snowflake or cloud-based data eco
• systems.Experience with responsible AI, model governance, bias detection, explainability, and audit requi
• rements.Experience supporting AI governance councils, architecture reviews, or model risk review pr
• ocesses.Experience with healthcare data domains such as members, providers, claims, benefits, eligibility, call center, clinical, dental, or operation

al data.