Machine Learning Engineer
DEKA Research and Development
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 applications Build and deploy production ML models across the full lifecycle from feature engineering and training through evaluation, deployment, and monitoring Develop and maintain event-driven distributed pipelines using Apache Kafka, Apache Spark, and related technologies Architect systems that integrate ML models with rule-based decision engines for automated, real-time decisioning Collaborate across disciplines to translate complex requirements into reliable, elegant engineering solutions Contribute to AI/LLM-driven workflows and orchestration systems that push the boundaries of what software can do Required Experience / Knowledge M.S. in Computer Science, AI, or a related field 6+ years in backend software engineering and distributed systems Strong proficiency in Java (Spring Boot, Spring MVC, Hibernate) and Python Hands-on experience building and deploying production ML models (PyTorch or equivalent) Experience with distributed systems and streaming technologies: Apache Kafka, Apache Spark, ZooKeeper Solid understanding of microservices architecture, REST/SOAP APIs, and object-oriented design Experience with relational and NoSQL databases (Oracle, IBM Db2, MSSQL, MongoDB) Familiarity with AWS or equivalent cloud platforms Additional Skills Strong problem-solving skills Intrinsic drive to understand how things work and make them better Excellent communicating and collaboration across engineering, data, and product teams #J-18808-Ljbffr
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