MLOps/DevOps Engineer

Job Description

• Warehousing and logistics systems play an increasingly critical role in contributing to the
  competitiveness of many companies and, at the same time, to the effectiveness of the
  general world economy.
• The modern intra-logistics solutions combine state-of-the-art mechatronic, complex
  software, advanced robotics, modern computational perception, and sophisticated AI
  and operations research algorithms to provide high throughput and efficient processing
  for many missions’ critical commercial logistics applications.
• Our Warehouse Execution Software leverages advances in classical and modern
  optimization techniques to bring intelligent execution to the world of intralogistics and
  warehouse automation. We synchronize discrete and low-level logistics related
  processes to create a real-time decision engine that drives labor and equipment at the
  highest efficiency. Our software provides customers with the operational agility they
  need to efficiently handle the demands of an Omni-channel environment.
• We are looking for a highly motivated individual who can develop cutting edge MLOps
  and DevOps frameworks to deploy AI models.
• The candidate should have a solid grasp of state-of-the-art cloud technologies, best in
  class deployment architectures/frameworks and production grade software. The
  candidate must have an open mindset regarding the tradeoffs between coding from
  scratch versus utilizing existing frameworks. Many of the problems we encounter are
  novel and have never been solved before, so creative, out-of-the-box thinking and a
  fondness for experimentation are a must.
• We also want someone who stays current with recent trends in MLOps/DevOps so our
  approaches remain the most robust and competitive in the industry. Finally, the role
  requires strong team and interdisciplinary collaboration to see products through the
  development cycle from beginning to end.

 

Core Job Responsibilities

• Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data
  transformation, model training, model validation, model serving, and model evaluation
  over time.
• Collaborate closely with AI scientists to accelerate productionisation of ML algorithms.
• Setup CI/CD/CT pipelines for ML algorithms.
• Deploy models as a service both on-cloud and on-prem.
• Learn and apply new tools, technologies, and industry best practices.
  

  Key Qualifications

• MS in Computer Science, Software Engineering, or equivalent fiel
• Experience with Cloud Platforms, especially GCP and Azure, and related skills: Docker, Kubernetes, edge computin
• Familiarity with task orchestration tools such as MLflow, Kubeflow, Airflow, Vertex AI, Azure ML, etc
• Fluency in at least one general purpose programming language. Python or Java preferred. Strong DevOps skills: Linux/Unix environment, testing, troubleshooting, automation, Git, , dependency management, and build tools (GCP Cloud Build, Jenkins, Gitlab CI/CD, Github Actions, etc.).
• Data engineering skills a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow etc.
• 5+ years of experience, including academic experience, in any of the above.
• To Adhere to the Information Security Management policies and procedures.