Machine Learning Engineer

We're looking for a Machine Learning Engineer who can operate at the intersection of backend engineering and applied machine learning. If you want to design distributed systems, deploy production ML models, and architect scalable data pipelines that make a measurable difference — this is your opportunity, and it starts with the software you design and deliver at DEKA.This role is on-site at DEKA in Manchester, NH. Relocation is not available, local candidates only.How You Will Make an Impact:Design and implement scalable backend services and microservices powering data-intensive, real-world applicationsBuild and deploy production ML models across the full lifecycle from feature engineering and training through evaluation, deployment, and monitoringDevelop and maintain event-driven distributed pipelines using Apache Kafka, Apache Spark, and related technologiesArchitect systems that integrate ML models with rule-based decision engines for automated, real-time decisioningCollaborate across disciplines to translate complex requirements into reliable, elegant engineering solutionsContribute to AI/LLM-driven workflows and orchestration systems that push the boundaries of what software can doRequired Experience/Knowledge:M.S. in Computer Science, AI, or a related field6+ years in backend software engineering and distributed systemsStrong proficiency in Java (Spring Boot, Spring MVC, Hibernate) and PythonHands-on experience building and deploying production ML models (PyTorch or equivalent)Experience with distributed systems and streaming technologies: Apache Kafka, Apache Spark, ZooKeeperSolid understanding of microservices architecture, REST/SOAP APIs, and object-oriented designExperience with relational and NoSQL databases (Oracle, IBM Db2, MSSQL, MongoDB)Familiarity with AWS or equivalent cloud platformsStrong problem-solving skillsIntrinsic drive to understand how things work and make them betterExcellent communicating and collaboration across engineering, data, and product teamsStrong attention to detail