Machine Learning Engineer

Job Title : Machine Learning Engineer Location: Westbrook, Maine TOP (3) REQUIRED SKILLSETS: • Software Engineering - Python • Demonstratable experience with implementing LLM agents or working with evaluation frameworks • Strong collaboration and communication skills (collaborate with Data Science, product owners, PMO) NICE TO HAVE SKILLSETS: • Databricks/Spark • DevOps (AWS cloud including EC2, Lambda, Cloudformation) • Familiarity with logging, monitoring, or observability tools for ML systems. We are seeking a Machine Learning Infrastructure Engineer to design and build robust, scalable systems that support the development, evaluation, and deployment of cutting-edge AI solutions, with a focus on large language models (LLMs). You will work closely with data scientists, ML engineers, and product teams to create tools and frameworks that streamline the ML lifecycle—from data annotation to model evaluation and feedback collection. This role is ideal for someone passionate about building the foundational infrastructure that enables high-quality, reproducible, and efficient ML workflows. The Machine Learning Operations team supports all projects in the Center of Excellence, and you will contribute to high impact problems. If you are passionate about machine learning and invigorated by our mission to enhance the health and well-being of pets, people and livestock, this is the role for you! What you will do: • Design and implement LLM evaluation frameworks to support automated and human-in-the-loop assessment of model performance. • Build custom feedback tools to collect structured and unstructured user feedback on model predictions. • Develop systematic analysis tools for logged predictions, enabling deep dives into model behavior, error patterns, and performance trends. • Create and maintain tooling and infrastructure that supports the end-to-end ML lifecycle, including data preparation, annotation, training, evaluation, and monitoring. • Collaborate with cross-functional teams to integrate evaluation and feedback tools into production ML pipelines. • Ensure scalability, reliability, and usability of ML infrastructure across teams and projects. What you need: • 3+ years of experience in ML infrastructure, MLOps, or backend engineering for ML systems. • Strong programming skills in Python and experience with ML/DS libraries (e.g., PyTorch, TensorFlow, scikit-learn). • Deep understanding of ML evaluation methodologies, especially for LLMs and generative models. • Experience with Databricks for data engineering, model training, and collaborative workflows. • Hands-on experience with SuperAnnotate or similar data annotation platforms. • Proficiency with AWS services (e.g., S3, Lambda, SageMaker, ECS) for scalable ML infrastructure. • Familiarity with logging, monitoring, and observability tools for ML systems. • Strong communication and collaboration skills.