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Founded 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 Automation, we specialize in digitizing healthcare processes to optimize 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 all 50 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. 

Machine Learning Engineer

We 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. Machine 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.

Job Role and Responsibilities

• Develop and train machine learning models to address defined business or research problems using appropriate algorithms and techniques.
• Build and maintain data pipelines that feed data into Machine Learning (ML) models, including data collection from databases or APIs and data preprocessing.
• Deploy machine learning models into production environments (cloud or on-premises) and create APIs or interfaces for other applications to interact with these models.
• Monitor and evaluate model performance in production, tuning parameters or updating data as needed to improve accuracy and efficiency.
• Collaborate closely with data scientists to understand project objectives, and with software engineers or IT teams to integrate ML solutions into broader systems.
• Write clean, efficient, and maintainable code using version control tools (e.g., Git); participate in code reviews and follow best practices in development.

Required Qualifications

• Bachelor’s degree in computer science, Data Science, Engineering, or a related field (a Master’s degree is an added advantage).
• Approximately 1–2+ years of hands-on experience in machine learning engineering or software development with a focus on data or ML projects.
• Proven experience in developing machine learning models through professional or significant academic projects, including training, validation, and evaluation techniques.
• Strong programming skills in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience 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.

Skills and Competencies

Technical Skills

• API development and containerization using Flask, FastAPI, and Docker
• Databases: SQL (MSSQL, MySQL) and NoSQL (MongoDB)
• Cloud platforms: AWS / GCP (S3, Lambda, SageMaker)
• Monitoring tools: Prometheus, Grafana
• ML frameworks: TensorFlow, PyTorch, XGBoost

Machine Learning Expertise

• Strong understanding of machine learning algorithms (regression, classification, clustering, deep learning) and their appropriate use cases.

Software Engineering

• Ability to write production-quality code, optimize performance, and apply software engineering best practices, including testing and containerization.

Data Handling

• Proficiency in data manipulation and analysis; ability to handle noisy or unstructured data and transform it for model training and inference.

Problem-Solving

• Strong analytical skills to troubleshoot model issues, identify improvements, and develop effective technical solutions.

Communication & Teamwork

• Ability to clearly explain machine learning concepts to non-technical stakeholders and collaborate effectively within cross-functional teams.

Continuous Learning

• Commitment to staying current with advancements in machine learning and AI, and proactively learning new tools, techniques, and methodologies.