Ss&c
Financial Services, Healthcare Technology
PrincipalMLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Principal ML Engineer at Ss&c. Skills: ML Engineering, MLOps, Cloud-native ML, Platform development. Build scalable ML deployment pipelines. Design cloud-native ML workflows”
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
8+ years relevant experience, Bachelor's degree in Computer Science, Python programming skills, Flask, Django, FastAPI, or Celery experience, ML SDLC experience, Microservices architecture experience, Productionizing Python or Java applications, AWS (EC2, S3, Data Lake) experience, Kubernetes experience, CI/CD tooling experience, Jenkins, Terraform, Splunk, Grafana experience, ML frameworks (PyTorch, Keras, scikit-learn) experience, End-to-end data and ML pipelines experience, RESTful API development experience, Containerized deployments (Docker/Kubernetes) experience, Scalable ML models in production experience, Linux proficiency, Software engineering fundamentals, MongoDB, PostgreSQL, Milvus, Chroma, Pinecone experience
Nice to Have
Master's degree in Computer Science, Big data and ML orchestration tools experience, Spark, Dask, Kubeflow, or Airflow experience, GCP, Azure experience, Snowflake experience, Microservices architectures design experience, Distributed systems at enterprise scale experience
What You'll Do.
Build scalable ML deployment pipelines
Design cloud-native ML workflows
Develop tooling for ML lifecycle
Create RESTful APIs for model management
Operationalize ML solutions
Bridge research and production gap
Design deployment infrastructure
Maintain CI/CD pipelines
Automate ML workflows
Support continuous delivery
Lead methodology improvements
Drive technical standards
Mentor junior engineers
Provide production support
Ensure site reliability for ML systems
Minimize downtime and performance degradation
Own escalation workflows for incidents
Triage production issues
Coordinate incident resolution
Conduct root cause analysis
Implement preventive measures
How You'll Work.
Team & Collaboration
Partner with Data Scientists; Partner with Engineers; Mentor junior engineers
Process & Methodology
CI/CD
Full Job Description
As a leading financial services and healthcare technology company based on revenue, SS&C is headquartered in Windsor, Connecticut, and has 27,000+ employees in 35 countries. Some 20,000 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. **_Job Description_** Principal ML Engineer **Locations:** Waltham, MA (Hybrid) **About the Role** We are looking for a Principal 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 infr
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