AI Enablement Software Engineer I

Alegeus; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible work environment

Competitive salaries

Professional development

Health, wellness, and financial packs

Job summary

Alegeus is building an AI Enablement Engineering Team to integrate AI capabilities into its core fintech-healthcare products. As a Software Engineer, you will implement AI-driven features, APIs, and data pipelines, collaborating with product, data science, and platform teams to deliver production-ready solutions.

You will work with SLMs/LLMs, cloud-hosted and on-premise variants, and apply robust MLOps, CI/CD, and observability practices across the stack.

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 such as Azure, AWS, or GCP with working knowledge of cloud-native engineering and DevOps practices.
• Ability to explore and apply advanced AI techniques including Retrieval-Augmented Generation (RAG), Retrieval-Integrated Generation (RIG), Small Language Models (SLMs), and Model Context Protocol (MCP).

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.

Skills

Python

SQL

Pandas

NumPy

TensorFlow

PyTorch

Scikit-learn

ML Ops

Azure ML

Docker

Kubernetes

C#

ASP.NET Core

REST APIs

LLMs

SLMs

RAG

MCP

Tools

Docker

Kubernetes

Azure ML

MLflow

Job description

Alegeus is building an AI Enablement Engineering Team to integrate AI capabilities into its core fintech-healthcare products. As a Software Engineer, you will implement AI-driven features, APIs, and data pipelines, collaborating with product, data science, and platform teams to deliver production-ready solutions.

You will work with SLMs/LLMs, cloud-hosted and on-premise variants, and apply robust MLOps, CI/CD, and observability practices across the stack.