Principal Data & ML Engineer

SS&C is a leading provider of mission-critical, AI-powered technology and services empowering financial services and healthcare organizations to work smarter, faster, and securely. Founded in 1986, SS&C is headquartered in Windsor, Connecticut, and has offices worldwide. More than 23,000 financial services and healthcare organizations, from the world's largest companies to small and mid-market firms, rely on SS&C for expertise, scale, and technology.

Principal Data & ML Engineer Location: Waltham MA - 10 CityPoint (Hybrid)   About the Role We are looking for a Principal Data & ML Engineer to design, build, and operationalize machine learning platforms and pipelines that power real business outcomes. In this senior role, you will lead the development of model lifecycle infrastructure, cloud-native ML workflows, and automated deployment processes — while mentoring junior engineers and championing ML engineering best practices across the organization. Why Join SS&C SS&C combines proprietary technology with deep industry expertise to support complex financial and health care operations. Our teams design, implement, and operate solutions that help clients manage data, automate processes, and scale their businesses with confidence. You will work with industry experts, modern platforms, and evolving technologies, gaining exposure to real-world operational challenges and large-scale enterprise environments. How You Will Make an Impact  Build scalable, self-service ML model deployment pipelines that enable teams to move from experimentation to production with speed and reliability. Design cloud-native ML workflows aligned with organizational strategy and modern MLOps principles. Develop tooling for model development, deployment, monitoring, and reporting across the full ML lifecycle. Create and maintain RESTful APIs for model lifecycle management, ensuring scalability, security, and reliability. Partner with Data Scientists and Engineers to operationalize ML solutions and bridge the gap between research and production. Design and maintain deployment infrastructure, CI/CD pipelines, and automated ML workflows to support continuous delivery. Lead methodology improvements, drive technical standards, and mentor junior engineers across data and ML engineering teams. Provide production support and ensure site reliability for deployed ML systems, including proactive monitoring, alerting, and incident response to minimize downtime and performance degradation. Own escalation workflows for production incidents — triage issues, coordinate resolution across teams, conduct root cause analysis, and implement preventive measures to improve system stability.

8+ years of relevant experience in data engineering, ML engineering, or a related field, with a Bachelor’s degree in Computer Science or a quantitative discipline. Strong Python programming skills with hands-on experience using frameworks such as Flask, Django, FastAPI, or Celery. Solid experience with ML SDLC, microservices architecture, and productionizing Python or Java applications. Hands-on experience with AWS (EC2, S3, Data Lake), Kubernetes, and CI/CD tooling including Jenkins, Terraform, Splunk, and Grafana. Familiarity with ML frameworks (PyTorch, Keras, scikit-learn) and experience building end-to-end data and ML pipelines. Proven experience with RESTful API development, containerized deployments (Docker/Kubernetes), and delivering scalable ML models in production. Linux proficiency, strong software engineering fundamentals, and experience with databases including MongoDB, PostgreSQL, Milvus, Chroma, and Pinecone. What Sets You Apart (preferred qualifications) Master’s degree in Computer Science, Data Science, or a related quantitative field. Experience with big data and ML orchestration tools such as Spark, Dask, Kubeflow, or Airflow. Familiarity with additional cloud platforms (GCP, Azure) and data warehousing solutions such as