Senior Software Engineer, Pipeline Team

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, construct, and sustain comprehensive ML pipelines that encompass data ingestion, preprocessing, model training, evaluation, and serving.

• Take ownership of model versioning, data versioning, and prompt versioning across various environments.

• Develop and manage deployment infrastructure for side-by-side operations and A/B testing frameworks to rigorously assess model variants in production.

• Implement mechanisms for drift detection, monitor data quality, and set up alerting throughout the pipeline stack.

• Enhance CI/CD practices within the ML lifecycle—automating training triggers, evaluation gates, and deployment workflows that integrate with the overall engineering delivery pipeline.

• Design and execute robust, high-performance, and secure ML infrastructure.

• Mentor and guide junior engineers, promote a culture of knowledge-sharing, and influence ML engineering best practices at both the team and organizational levels.

• Ensure code quality through peer reviews, unit testing, and compliance with coding standards across both pipeline and platform code.

• Collaborate closely with ML scientists, product managers, and infrastructure teams to convert model development requirements into reliable production systems.

• Ensure that pipelines and model artifacts adhere to best security practices and comply with industry standards relevant to legal document processing.

• Maintain comprehensive technical documentation for pipelines, model registry conventions, and operational runbooks.

⛳️ Requirements

• 5+ years of experience in software engineering, with a minimum of 2–3 years in ML engineering, MLOps, or AI platform roles.

• Practical experience with model versioning, data versioning, prompt versioning, experiment tracking, and automation of deployments in production settings.

• Expertise in workflow orchestration (such as Apache Airflow or a similar tool), experiment tracking (like MLflow or its equivalent), and cloud-based model hosting (AWS Bedrock or equivalent).

• Background in designing and managing side-by-side deployments, shadow mode evaluations, canary releases, and automated rollback strategies for ML models.

• Knowledge of model drift detection, data quality monitoring, and pipeline alerting; experience in defining and tracking ML-specific SLOs.

• Familiarity with AWS services (including S3, ECS/EKS, Lambda, and Step Functions or their equivalents); comfortable operating within a cloud-native environment.

• Proficient in Python, capable of writing scalable, maintainable, and secure code.

• Experience with SQL and familiarity with data engineering patterns is advantageous.

• Experience in extending CI/CD principles to ML workflows, such as automated training pipelines, evaluation gates, and model promotion processes.

• Skilled in designing modular, high-performance systems; capable of making technical decisions and clearly articulating trade-offs.

• Implements automated testing for pipeline components and values reproducibility and reliability in ML systems.

• Addresses ambiguous, complex challenges while evaluating trade-offs among performance, reliability, and development speed.

🏝️ Benefits

• Health insurance

• 401(k) matching

• Flexible work hours

• Paid time off

• Professional development opportunities