PpbJoin one of the BEST WORKPLACES in Sri Lanka! /b /p pFounded in 2011, SYNERGEN Health is a leading provider of transformational revenue cycle solutions and services in the healthcare industry. Leveraging innovative Analytics, Artificial Intelligence/Machine Learning, and Robotic Process Automation, we specialize in digitizing healthcare processes to maximize revenue potential. Our technology-driven approach reimagines revenue cycle management, enabling our client partners to achieve unprecedented efficiency, cost savings, and value and best serve their communities. With a presence in over 45 states in the USA, our mission is to catalyze change in the healthcare industry, collaborating closely with our clients to lower the cost of collections while upholding the highest compliance standards. We are dedicated to driving positive change as we continuously strive to transform ideas into new and improved solutions, services, and prescriptive processes. /p pSYNERGEN Health has been ranked among the 5,000 fastest-growing private companies in the U.S. for seven consecutive years by Inc. magazine. Additionally, SYNERGEN Health was recognized as one of the 70 Best Large Workplaces in Asia by Great Place to Work® in 2025. /p pSYNERGEN Health (Pvt) Ltd’s solutions team was formed with a mission to transform the U.S. Healthcare industry with breakthrough products. We deliver solutions to enterprise clients using latest technology, an agile approach that drives collaboration, innovation and excellence. /p h3Machine Learning Engineer /h3 pWe are looking for a Machine Learning Engineer, a mid-level professional responsible for designing, developing, and deploying machine learning models and systems. This role is critical in transforming data science prototypes and ideas into robust, scalable applications. /p pMachine Learning Engineers bridge the gap between model development and software engineering, ensuring that machine learning solutions perform effectively in production and deliver measurable value to the organization. /p h3Job Role and Responsibilities /h3 ul liDevelop and train machine learning models to address defined business or research problems using appropriate algorithms and techniques. /li liBuild and maintain data pipelines that feed data into Machine Learning (ML) models, including data collection from databases or APIs and data preprocessing. /li liDeploy machine learning models into production environments (cloud or on-premises) and create APIs or interfaces for other applications to interact with these models. /li liMonitor and evaluate model performance in production, tuning parameters or updating data as needed to improve accuracy and efficiency. /li liCollaborate closely with data scientists to understand project objectives, and with software engineers or IT teams to integrate ML solutions into broader systems. /li liWrite clean, efficient, and maintainable code using version control tools (e.g., Git); participate in code reviews and follow best practices in development. /li /ul h3Required Qualifications /h3 ul liBachelor’s degree in computer science, Data Science, Engineering, or a related field (a Master’s degree is an added advantage). /li liApproximately 1-2+ years of hands‑on experience in machine learning engineering or software development with a focus on data or ML projects. /li liProven experience in developing machine learning models through professional or significant academic projects, including training, validation, and evaluation techniques. /li liStrong programming skills in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit‑learn. /li liExperience working with databases (SQL or NoSQL) and large datasets; familiarity with cloud platforms or big data tools (e.g., AWS, Azure, or GCP) is advantageous. /li /ul h3Skills and Competencies /h3 h3Technical Skills /h3 ul liAPI development and containerization using Flask, FastAPI, and Docker /li liDatabases: SQL (MSSQL, MySQL) and NoSQL (MongoDBli /liliCloud platforms: AWS / GCP (S3, Lambda, SageMaker) /li liMonitoring tools: Prometheus, Grafana /li liML frameworks: TensorFlow, PyTorch, XGBoost /li /ul h3Machine Learning Expertise /h3 ul liStrong understanding of machine learning algorithms (regression, classification, clustering, deep learning) and their appropriate use cases. /li /ul h3Software Engineering /h3 liAbility to write production-quality code, optimize performance, and apply software engineering best practices, including testing and containerization. /li h3Data Handling /h3 ul liProficiency in data manipulation and analysis; ability to handle noisy or unstructured data and transform it for model training and inference. /li /ul h3Problem-Solving /h3 ul liStrong analytical skills to troubleshoot model issues, identify improvements, and develop effective technical solutions. /li /ul h3Communication Teamwork /h3 ul liAbility to clearly explain machine learning concepts to non-technical stakeholders and collaborate effectively within cross-functional teams. /li /ul h3Continuous Learning /h3 ul liCommitment to staying current with advancements in machine learning and AI, and proactively learning new tools, techniques, and methodologies. /li /ul /p #J-18808-Ljbffr