Hybrid ML Engineer: Scalable Data Pipelines & AI Services

Manulife Insurance Malaysia

Boston (MA)

Hybrid

USD 90,000 - 167,000

Full time

4 days ago

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Job summary

John Hancock Life Insurance Company (USA) is seeking a Machine Learning Engineer to build and maintain scalable data pipelines and backend services for underwriting, analytics, and ML workloads. You will deploy ML pipelines in production, diagnose data and model issues, and collaborate with data scientists, underwriters, and engineers to translate business needs into technical solutions.

The role requires a Master’s degree and 3+ years of Python, ML framework, and backend API experience, with a

Qualifications

Master's degree or foreign equivalent in Data Science, Information Studies, Information Science, Computer Science, Machine Learning, or a closely related field and 3 years of Python programming experience for backend systems, automation, or machine learning workloads.
3 years of experience using SQL or NoSQL databases including PostgreSQL or MongoDB for data modeling, querying, or pipeline integration.
3 years of experience developing or deploying machine learning models using PyTorch or TensorFlow.
3 years of experience building or maintaining backend APIs or microservices using FastAPI or Flask.
2 years of experience deploying ML workflows or backend services using AWS or Azure.
2 years of experience using containerization and orchestration tools including Docker and Kubernetes.
2 years of experience using GitHub Actions or TeamCity for CI/CD.
2 years of experience designing distributed or scalable systems including microservices, load balancing or distributed processing.

Responsibilities

Develop, build and maintain scalable data pipelines supporting underwriting, analytics, and machine learning systems; Develop and deploy backend services and microservices to support data ingestion, retrieval, and ML inference workflows; Deploy, monitor, and maintain machine learning pipelines and backend services in production environments; Conduct root‑cause analysis of data, model, and pipeline issues and implement corrective solutions; Collaborate with data scientists, underwriters, and engineering teams to translate business requirements into technical solutions; and Support production releases and large‑scale batch processing of underwriting workflows.