Hiring: Senior Machine Learning Engineer - Hybrid Company: Manulife (John Hancock Life Insurance Company USA) Location: Boston, MA (Hybrid – 3 days in office, 2 days remote) Job Posted Time: 2026-09-16 09:59:37 Employment Type: Full-time Target Skills & Keywords: MLOps, LLMOps, Machine Learning, Generative AI, LLM, RAG, Prompt Engineering, Azure AI Studio, Python, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, Docker, Kubernetes, AWS, Azure, GCP, Apache Spark, PySpark, Databricks, EMR, PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, Elasticsearch, Redis, BERT, GPT, T5, LLaMA, NLP, ETL, Feature Engineering, Fraud Detection, Compliance, Claims Adjudication, Automated Decision-Making Experience: - Master's degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field and 3 years of machine learning experience - 3 years of experience developing and deploying machine learning models for model training, optimization, evaluation, and production deployment through APIs, microservices, or cloud-based serving infrastructure - 3 years of experience deploying and managing infrastructure using Linux OS, containerization technologies, relational databases, and NoSQL databases on AWS, Azure, or GCP - 3 years of experience designing and building scalable ETL pipelines and feature engineering workflows for large-scale datasets using distributed processing frameworks - 2 years of experience developing and deploying Large Language Models or other transformer-based NLP models - 3 years of experience designing hybrid machine learning systems combining rule-based decision engines with ML models in regulated environments - 3 years of experience applying machine learning algorithms and statistical models Required Skills: - Design, recommend, and implement platforms and infrastructure using MLOps/LLMOps best practices - Collaborate with data scientists and data engineers to design and implement scalable and efficient machine learning pipelines - Evaluate and optimize machine learning models for performance and scalability - Deploy machine learning models into production and monitor performance - Manage data science infrastructure to streamline model development and deployment - Support development and deployment of high-quality Generative AI technologies including prompt engineering and RAG applications, and fine-tuning LLM models using Azure AI Studio - Propose appropriate tools including languages, libraries, and frameworks for implementing projects - Work closely with infrastructure architects to design scalable and efficient solutions - Collaborate with cross-functional teams to integrate machine learning models into existing systems and processes - Keep abreast of latest advancements in machine learning, MLOps, and LLMOps techniques - Mentor associates and peers on MLOps best practices Qualifications: - Master's degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field - 3 years of machine learning experience - Experience with Python, TensorFlow, PyTorch, Scikit-learn, Keras, or XGBoost - Experience with Linux OS, Docker, Kubernetes, PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, Elasticsearch, Redis - Experience with AWS, Azure, or GCP cloud platforms - Experience with Apache Spark (PySpark, Spark SQL), Hadoop ecosystem tools, Databricks, or EMR - Experience with BERT, GPT-series, T5, LLaMA, or other transformer-based NLP models - Experience in fraud detection, compliance, claims adjudication, or automated decision-making in regulated environments Compensation: Not specified in the job posting. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!