Expert Software Engineer I

Do you want to shape the future of fintech and healthtech? Energized by challenges and inspired by bold goals? Ready to elevate your career alongside driven and talented colleagues? If that sounds like you, explore a career at Alegeus today. Opportunity Happens Here .

About The Role We are forming an AI Enablement Engineering Team—focused on integrating foundational AI capabilities (such as document extraction and LLM-based chatbot services) into our core products.

This team will work closely with product domain engineering teams and foundational AI teams to deliver end-to-end product features—like claims extraction built on our document intelligence service.

As a Software Engineer in this team, you’ll be expected to apply strong software engineering practices, leverage AI services, and work across integration points to bring intelligent automation into production systems.

Key Responsibilities

• Design, develop, and integrate AI-driven capabilities into scalable, production-grade healthcare platforms to improve automation, insights, and user experience.
• Build robust APIs, microservices, and integration layers that seamlessly connect AI and ML models with product workflows and enterprise systems.
• Apply strong data science skills including EDA, statistical analysis, hypothesis testing, advanced feature engineering, and model evaluation to shape and optimize ML solutions.
• Work hands-on with SLMs and LLMs, including locally hosted and cloud-deployed variants, optimizing performance, latency, and domain-specific use cases.
• Collaborate with foundational AI and platform teams to leverage reusable shared services such as extractors, conversational agents, embedding services, and retrieval pipelines.
• Use Python, SQL, Pandas, NumPy, TensorFlow, PyTorch, and Scikit-learn to build, evaluate, and deploy ML models across classification, forecasting, anomaly detection, and NLP use cases.
• Build and maintain ML pipelines for training, evaluation, deployment, monitoring, and retraining using MLOps tools and cloud-native patterns.
• Develop clean, reliable, and maintainable software leveraging Python, REST APIs, containers, and cloud-native engineering practices aligned with enterprise standards.
• Contribute to backend services using C# and ASP.NET Core to support AI workflows, platform integrations, and service orchestration.
• Collaborate effectively with Product Managers, Architects, Data Scientists, and Engineering teams to deliver end-to-end AI solutions.
• Adhere to engineering best practices across code quality, automated testing, CI/CD pipelines, observability, monitoring, and documentation.
• Participate in agile development sprints and contribute to sprint planning, reviews, retrospectives, and demos.

Required Qualifications

• 6+ years of experience in AI engineering, ML engineering, data science, or software engineering within enterprise or SaaS environments.
• Proficient in Python, SQL, Pandas, NumPy, and modern ML frameworks such as TensorFlow, PyTorch, and Scikit-learn for data processing, feature engineering, model development, and evaluation.
• Strong experience applying ML algorithms including Random Forest, Gradient Boosting, logistic regression, anomaly detection, and advanced predictive modeling techniques.
• Hands-on experience in data science workflows including EDA, hypothesis testing, error analysis, statistical modeling, feature engineering, and model interpretation using techniques such as SHAP.
• MLOps experience with model training pipelines, experiment tracking, versioning, CI/CD for ML systems, monitoring, and deployment using Azure ML, MLflow, Docker, Kubernetes, or cloud inference endpoints.
• Experience building and operating end-to-end ML workflows from dataset creation and model training to deployment, monitoring, drift detection, and iterative improvements.
• Background in backend development using C# and ASP.NET Core for services, integrations, and API development supporting AI capabilities.
• Experience designing and consuming RESTful APIs for scalable and reliable communication across distributed and cloud-native systems.
• Familiarity with AI and ML technologies including LLMs, SLMs, embeddings, prompt engineering, retrieval pipelines, and AI service orchestration.
• Hands-on experience integrating backend services, ETL pipelines, data APIs, and enterprise data platforms to support analytics and AI solutions.
• Exposure to cloud platforms…