Senior Machine Learning Engineer - Hybrid

Overview In this role you will advance machine learning capabilities for John Hancock’s insurance division, building scalable pipelines and production-ready analytics. You will collaborate with data scientists and engineers to deploy models, monitor performance, and operationalize AI solutions, including generative AI and RAG applications. You will work with cross-functional teams to integrate ML into existing systems, mentor peers, and stay at the forefront of MLOps and LLMOps. This is a hybrid role based in Boston with a strong focus on business impact through robust data-driven solutions. Compensation / Benefitshealth, dental, mental health, visiondisability and life insuranceretirement plans (pension/401(k)) and global share ownershippaid time off: holidays, personal days, vacation, sick timeleaves of absence as required by lawwellness and employee assistance programs Responsibilities Design and implement platforms and infrastructure following MLOps/LLMOps best practices Collaborate to build scalable ML pipelines and production-ready models Evaluate, optimize, and deploy ML models; monitor performance in production Manage data science infrastructure to streamline model development and deployment Contribute to Generative AI initiatives, including prompt engineering and RAGRecommend tools, languages, libraries, and frameworks for projects Work with infrastructure architects to ensure scalable, efficient solutions Integrate ML models into existing systems and processes across cross-functional teams Stay updated on ML, MLOps, and LLMOps advancements and raise capabilities Mentor associates and peers on MLOps best practices Key requirements Master in Data Science, Computer Science, Computer Engineering, or related field 3 years of machine learning experience 3 years developing and deploying ML models via APIs, microservices, or cloud-based serving infrastructure 3 years deploying and managing infrastructure with Linux, Docker, Kubernetes, and databases (PostgreSQL, MySQL, Oracle) and NoSQL (MongoDB, Cassandra, Elasticsearch, Redis) on AWS, Azure, or GCP3 years designing scalable ETL pipelines and feature engineering with Spark, Hadoop, Databricks, or EMR2 years developing and deploying Large Language Models (LLMs) such as BERT, GPT-series, T5, or LLaMA3 years designing hybrid ML systems for fraud detection, compliance, or automated decision-making in regulated environments 3 years applying ML algorithms and statistical modeling to financial services/regulated industries 3 years Agile development (Scrum, Kanban, or SAFe)2 years CV/vision for document processing using OpenCV, Tesseract, or cloud vision APIsCollaborative mindset Mentoring and knowledge sharing Cross-functional communication Python Tensor Flow PyTorch