Expert Software Engineer I
Alegeus · Bengaluru · onsite · Posted 61d ago
About a minute. You'll make an account on the way through.
About this role
- 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.
- 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).
- Experience integrating AI services such as OpenAI, Azure OpenAI, or Hugging Face models into production systems.
- Knowledge of document extraction, conversational systems, NLP pipelines, or multimodal AI workflows.
- Experience with ML observability, model monitoring, cost optimization, and production-level inference tuning.
- Exposure to healthcare IT or healthcare data standards such as claims processing, HL7, EDI 837, EDI 835, or FHIR.
- Familiarity with feature stores, vector databases, and reusable ML components for cross-product AI enablement.
- Strong communication and collaboration skills, with the ability to work across global engineering and product teams.
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About Alegeus
Alegeus
FinTech
501-1000
employees
1995
31 years old
Waltham
US
About
Alegeus Technology
Alegeus is a FinTech company operating in the healthcare benefits space. At a high level, were the leading platform that powers tax-advantaged benefit accounts so think of things like HSAs, FSAs, etc. In other words, we sit at the intersection of healthcare, payments, and technology, and essential we're helping millions of people save smarter and spend better on their healthcare.
In recent years, Alegeus has grown quickly and successfully, but like many fast-growing companies, we’ve reached that stage where we need to evolve from “scale-first strategy” to sustainable, scalable growth. We’re sharpening our identity as a true FinTech company, not just HealthTech and we’re building the operational and strategic infrastructure to support that future.
Since joining Vista Equity Partners, one of the world’s leading private equity firms focused exclusively on software and technology with approx. 80 companies in their portfolio, we’ve been laying the groundwork for that next phase of growth and maturity. We’re investing in technology (think product, AI, etc.) , leadership, and process, all with the goal of creating a more aligned, efficient, and high-performing organization. When it comes to the numbers here's a rapid fire breakdown:
- 1k employees, HQ in Boston, but also have office in Bangalore and Orlando and a presence in Milwaukee.
- We've doubled revenue in five years.
- Directionally we're $300m + this year
- We've tripled EBITDA in five years with 47% EBITDA margins
- We're projected to have more than 11M lives on platform this year, with ~99% client retention
- Greater than 50% rule of 40
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Employee ratings
44 reviews
Culture
2.5
Career growth
2.2
Work-life
2.9
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