Key Responsibilities:

• Design, implement, and maintain robust MLOps platforms and tooling for both batch and streaming ML pipelines.
• Develop and manage monitoring and observability solutions for ML systems.
• Lead DevOps practices, including CI/CD pipelines and Infrastructure as Code (IaC).
• Architect and implement cloud-based solutions on AWS.
• Collaborate with ML Engineers and Data Scientists to develop, train, and deploy machine learning models.
• Engage in feature engineering and model optimization to improve ML system performance.
• Participate in the full ML lifecycle, from data preparation to model deployment and monitoring.
• Optimize and refactor existing systems for improved performance and reliability.
• Drive technical initiatives and best practices in both MLOps and ML Engineering.

Required Skills and Experience:

• Strong Python Proficiency: Excellent skills for developing, deploying, and maintaining our machine learning systems.
• Language Versatility: Experience with statically-typed or JVM languages. Willingness to learn Scala is highly desirable.
• Cloud Engineering Skills: Extensive experience with Cloud Platforms & Services, ideally AWS (e.g., Lambda, ECS, ECR, CloudWatch, MSK, SNS, SQS).
• Infrastructure as Code: Proficiency in IaC, particularly Terraform.
• Kubernetes Expertise: Strong hands-on experience with managing clusters and deploying services.
• Data Orchestration: Experience with ML orchestration tools (e.g., Flyte, Airflow, Kubeflow, Luigi, or Prefect).
• CI/CD: Expertise in pipelines, especially GitHub Actions and Jenkins.
• Networking: Knowledge of concepts and implementation.
• Streaming: Experience with Kafka and other streaming technologies.
• ML Monitoring: Familiarity with observability tools (e.g., Arize AI, Weights and Biases).
• NLP/LLMs: Experience with NLP, LLMs, and RAG systems in production, or strong desire to learn.
• CLI & Shell Scripting: Proficiency in scripting and command-line tools.
• APIs: Experience with deploying and managing production APIs.
• Software Engineering Best-Practices: Knowledge of industry standards and practices.

Preferred Qualifications:

• AWS AI Services: Hands-on experience with AWS SageMaker and/or AWS Bedrock.
• Data Processing: Experience with high-volume, unstructured data processing.
• ML Applications: Familiarity with NLP, Computer Vision, and traditional ML applications.
• System Migration: Previous work in refactoring and migrating complex systems.
• AWS Certification: AWS Solution Architect Professional or Associate certification.
• Advanced Degree: Master’s degree in ML / AI / Computer Science.

Personal Qualities:

• Passionate about building developer-friendly platforms and tools.
• Thrives in a terminal-based development environment.
• Enthusiastic about creating production-grade, robust, reliable, and performant systems.
• Not afraid to dive into and improve complex existing solutions.
• Team player who works well with ML Engineers, Data Scientists, and management.
• Strong technical mentoring skills.
• Excellent problem-solving and communication skills.