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.
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