21 days ago
Toronto, Canada or Boston, MA, USAMid Level
H1B sponsor
Base Salary
$90k - $167k/yr
Responsibilities
- Design, build, deploy, and maintain production-ready AI and ML solutions for the Long-Term Care program.
- Create reusable ML and GenAI pipelines covering data processing, feature engineering, training, evaluation, deployment, monitoring, and retraining.
- Operationalize classification, regression, forecasting, risk scoring, segmentation, anomaly detection, RAG, prompt orchestration, document intelligence, summarization, and workflow automation solutions.
- Deploy AI services using containerization, CI/CD, automated testing, version control, and cloud-native infrastructure.
- Monitor model performance, data and model drift, bias, accuracy, latency, cost, reliability, and business indicators.
- Build observability capabilities including logging, tracing, metrics, alerts, dashboards, and service-level monitoring.
- Collaborate with Risk, Legal, Compliance, Security, Architecture, and Cloud teams on secure, compliant, explainable, and governed AI solutions.
- Integrate AI services with enterprise workflows through APIs, event-driven architecture, batch pipelines, and business applications.
- Evaluate AI tools and platforms, continuously improve solution quality and cost efficiency, and share emerging technology knowledge.
- Mentor junior engineers and data scientists on production engineering, testing, monitoring, MLOps, and LLMOps practices.
Requirements
- At least 3 years of experience in AI Engineering, ML Engineering, Software Engineering, Data Science Engineering, or a related technical role.
- Strong Python programming skills and experience building reliable, maintainable, production-quality code.
- Proven experience deploying ML or AI models into production cloud environments.
- Hands-on MLOps experience with model versioning, model registries, CI/CD, automated testing, monitoring, retraining workflows, and production support.
- Experience monitoring production model accuracy, drift, latency, stability, reliability, and business performance.
- Strong knowledge of traditional machine learning, predictive analytics, supervised and unsupervised learning, feature engineering, model evaluation, and experimentation.
- Practical experience with GenAI and LLM solutions, including prompt engineering, RAG, embeddings, vector search, evaluation, and guardrails.
- Experience with Azure or other cloud platforms and tools including Azure ML, Azure OpenAI, Databricks, MLflow, Docker, Kubernetes or AKS, GitHub Actions, or Azure DevOps.
- Strong SQL skills and experience with structured and unstructured data.
- Experience with ETL/ELT, Spark, Databricks, Delta Lake, data quality, and scalable data pipelines.
- Knowledge of API design, unit testing, integration testing, code reviews, documentation, and secure software development.
- Bachelor’s degree in a relevant technical field or equivalent industry experience is preferred; a relevant master’s or PhD is an asset.
- Experience with insurance, financial services, healthcare, Long-Term Care, claims, underwriting, risk management, model governance, responsible AI, explainability, fairness testing, or regulated AI is preferred.
- Experience with document intelligence, claims analytics, call center analytics, workflow automation, knowledge management, vector databases, search technologies, orchestration frameworks, agentic patterns, evaluation frameworks, and guardrails is preferred.
Benefits
- Hybrid working arrangement in Boston, Massachusetts.
- Eligible employees may receive health, dental, mental health, vision, disability, life, AD&D, adoption/surrogacy, wellness, and employee/family assistance benefits.
- Retirement savings plans, pension or 401(k) plans, global share ownership with employer matching, and financial education and counseling resources are available to eligible employees.
- Paid time off includes up to 11 U.S. holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time, plus statutory leaves.
- Employees may participate in incentive programs tied to business and individual performance.
Tech Stack
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