Principal ML Engineer

SS&C Technologies•Waltham, MA

4d•$150,000 - $160,000•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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 Snowflake.
Prior experience designing microservices architectures and working with distributed systems at enterprise scale.

Responsibilities

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.