Machine Learning Engineer

SYNERGEN Health is a US based company which pioneers in comprehensive financial solutions that enable healthcare organizations to maximize their revenue. The company provides tech-enabled services, advanced analytics, FinTech payment solutions, machine learning, consulting services and other software solutions. SYNERGEN Health was ranked as one of the 5000 fastest growing private companies in the US for five consecutive years by Inc.

SYNERGEN 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

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 crucial as it turns data science prototypes and ideas into robust, scalable applications. ML Engineers bridge the gap between model development and software engineering, ensuring that machinfe learning solutions perform well in production and deliver value to the organization.

Job Role and Responsibilities

• Develop and train machine learning models to solve defined business or research problems, using appropriate algorithms and techniques.
• Build and maintain data pipelines that feed data into ML models, including data collection from databases or APIs and preprocessing steps to transform data.
• Deploy 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 model parameters or updating data as needed to improve accuracy and efficiency.
• Work closely with data scientists to understand project objectives and with software engineers or IT to integrate ML solutions seamlessly into the broader system.
• Write clean, efficient code and use version control (e.g., Git) to collaborate on codebases; perform code reviews for peers and incorporate best practices in development.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (Master’s degree in a relevant field is a plus)
• Approximately 1-2+ years of hands-on experience in machine learning engineering or software development with a focus on data/ML projects
• Proven experience developing machine learning models (through professional projects or significant academic projects), including knowledge of training, validation, and evaluation techniques
• Strong programming skills in languages such as Python (Angular frontend development) and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
•  Experience with databases (SQL or NoSQL) and working with large datasets; familiarity with cloud platforms or big data tools (such as AWS, Azure) is advantageous.

Skills and Competencies

• Technical Skills: Docker, Flask, FastAPI in API development and containerization; SQL (MSSQL, MySQL), NoSQL (MongoDB); AWS/GCP (S3, Lambda, SageMaker); Monitoring: Prometheus, Grafana; ML Frameworks: TensorFlow, PyTorch, XGBoost;
• Machine Learning Expertise: Solid understanding of machine learning algorithms (regression, classification, clustering, deep learning) and when to apply them.
• Software Engineering: Ability to write production-quality code, optimize algorithms for performance, and use software engineering tools (testing frameworks, containerization with Docker, etc.).
• Data Handling: Proficiency in data manipulation and analysis; able to handle noisy or unstructured data and transform it for model consumption.
• Problem-Solving: Strong analytical thinking to troubleshoot model issues, identify improvements, and find creative solutions to technical challenges.
• Communication & Teamwork: Good communication skills to explain machine learning concepts to stakeholders; works effectively in cross-functional teams, collaborating with data scientists, engineers, and product managers.
• Continuous Learning: Keeps up-to-date with the latest developments in ML and AI, and is proactive about learning new tools or techniques that could improve models or workflows.

Career Progression: A successful ML Engineer can be promoted to Senior ML Engineer as they gain experience and take on more complex projects. Over time, they might specialize further or move into leadership roles such as Technical Lead in ML Engineering, guiding junior engineers and taking more responsibility in system design and project leadership.