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.
Job Location: Remote