Data Platforms, ML Engineer

Key Skills

Agile methodologies, Apache Hadoop, AWS, Continuous Integration & Continuous Delivery - CI/CD, DevOps, Effective communication skills, Employee life cycle, IT, Keras software library, Kubernetes-K8s, Large Language Models - LLMs, Machine learning techniques, MLOps tools, Natural Language Processing (NLP), Operations, Pandas, Presentation skills, Python Programming, PyTorch, Scikit-learn, SPARK Programming, TensorFlow, Transformer

Job Description

Position Summary:

Machine Learning (ML) Engineering and Operations is a new team in the Enterprise Data Platforms area, focused on operationalizing machine learning models and enabling ML solutions at scale. We seek an experienced ML Engineer eager to grow their skills in the ML Operations (MLOps) discipline. The ML Engineer will be hands on contributing to our MLOps capabilities, maintaining platforms, evolving software development kits, and continuously improving user experience. This role will contribute to building the ML Engineering discipline at BMS and providing ML Engineering services to assist data science teams operationalize their machine learning models. The ML Engineer we seek is experienced with enabling machine learning models and systems at scale, familiar with AWS services, and well versed in the growing field of Machine Learning Operations (MLOps).

Responsibilities: 

• Build ML pipelines to support experimentation, continuous integration and deployment (CI/CD), verification, validation and monitoring of ML models. 
• Build software toolchain, processes and templates for Data Science team for development and deployment environments. Hands-on development of platform components including python SDK, Rest APIs, and AWS infrastructure 
• Continuously evaluate the latest packages and frameworks in the ML ecosystem 
• Monitor resource usage and plan for increased capacity. 
• Keep effective communication with team members to ensure user and technical issues are promptly prioritized and resolved. 
• Coordinate the full lifecycle process for onboarding new data science project teams through completion, from initial request scoping through final deployment into support 
• Provide hands-on ML engineering support to data science teams 
• Advise and collaborate closely with domain data scientists to operationalize ML assets. 
• Conduct product demos and user training sessions. Create documentation and best practices to share with ML community. 
• Engage broadly with the organization to identify, prioritize, frame and structure DevOps requirements for various ML projects. 
• Contribute to maturing the ML Engineering discipline within BMS by developing leading practices and relevant thought leadership 
• Enable daily support activities for the MLOps platform and related capabilities
• Participate in the management of the backlog of platform enhancement requests in JIRA and provide guidance to offshore development teams, consultant partners, and product vendors 
• Collaborate with IT peers across Enterprise Applications and Platforms to identify opportunities to apply ML to our work in IT, contributing to proof of concepts and operationalizing ML assets that demonstrate value 

Qualifications: 

• B.S. in Computer Science or similar (Engineering, Math, Physics) preferred
• 4+ years of professional experience 
• 3+ years of software development experience delivering cloud-based solutions, preferably using AWS Cloud technologies 
• 3+ years of experience with Python with relevant experience in programming intensive role 
• Experience in distributed cloud-computing platforms and technologies like Kubernetes, Hadoop and Spark ecosystem 
• Experience deploying machine learning models into real-world cloud applications using MLOps leading practices, demonstrating some combination of the following skills: 
• Machine Learning Frameworks and Techniques 
• Machine Learning automation architectures at scale 
• Containerization 
• Distributed Data and Computing 
• Experience with Agile software development
• Knowledge of large-scale distributed software services and solutions 
• Experience building data access and visualization tools. 
• Experience with DevOps and CI/CD 
• Excellent communications skills (written and oral)
• Experience defining, driving, and executing low-level tasks to completion across multiple simultaneous activities 
• Demonstrated understanding and working knowledge of AWS Cloud technologies with preference to the following AWS certifications: Solutions Architect, Developer, DevOps Engineer, Data Analytics and/or Machine Learning 
• Experience with the most common ML/DL frameworks (Scikit-Learn, Pandas, Tensorflow, Transformers, PyTorch, Keras) 
• Knowledge of the most important classical machine learning models (Classification, Regression, Clustering, Time-Series Analysis, Dimensionality Reduction etc.) 
• Knowledge of deep learning models (Convolutional Neural Network, Recurrent Neural Network etc.) and other neural networks 
• Familiarity with modern computer vision, computational linguistic and/or transformer-based natural language processing (NLP), Natural Language Generation (NLG), Large Language Models (LLM) frameworks 
• Experience writing quality, well-documented and well-tested code. 
• Excellent communication and presentation skills