Lucd is a growing Enterprise AI software company that is passionate about democratizing AI. We are building our team to solve the biggest challenges which are slowing the adoption of artificial intelligence for government and industry across the globe.
- We are 100% remote
- We operate on flexible workdays with a goal-based culture
- We provide quality compensation packages
- We offer generous stock options to our team
Enterprise AI stakeholders need robust capabilities for enforcing AI governance in accordance with their specific business needs; for example, defining policies for identifying biased datasets or AI models, and assessing “degree of fitness” of a model for production deployment.
We are looking for a Machine Learning Engineer with a passion for Machine Learning Operations (MLOps) to support the development of a well-architected, highly performant, and intuitively configurable AI lifecycle management system as a core part of Lucd’s product strategy.
The primary responsibilities for this Machine Learning Engineer position will focus on developing machine learning operations (ML Ops) capabilities for the Lucd platform in support of the needs stated above. This primarily includes the following responsibilities:
- Contributing to the architecture and implementation of an extensible ML Ops framework enabling governance capabilities such as (but not limited to) data quality measurement, AI model experimentation, and AI production model monitoring, all facilitated in a customized and automated manner. The targeted AI production environments might include cloud-based API services or edge deployments (e.g., embedded or mobile devices).
- Contributing to product capabilities supporting data and model management for large-scale distributed and/or federated AI platforms.
- Contributing to product features further enhancing the no- to low-code AI development capabilities of the Lucd platform, i.e., translating common AI operations and workflows into graphical interfaces, and developing the backend microservices needed to support them
- B.S. in Computer Science, Data Science, or related field of study.
- Strong understanding of the overall AI development workflow, including ML Ops activities.
- Strong Python and C-based language development skills.
- Working knowledge of Linux systems.
- 3+ years of professional experience developing machine learning based solutions (including performing data engineering and data analysis activities) using leading enterprise-grade frameworks such as TensorFlow, PyTorch, XGBoost, Scikit-Learn, and MXNet.
- 2+ years of professional experience deploying and maintaining machine learning models in production environments.
- M.S. in Computer Science (with a focus on machine learning or data science), Data Science, or related field of study.
- Solid understanding of scalable (enterprise) cloud software frameworks, such as Kubernetes.
- 2+ years of experience with AI model and/or data bias detection and mitigation and addressing other related “AI ethics” topics.
- 2+ years of experience with using programming frameworks for distributed computation and data processing (e.g., Dask, Spark, RAPIDS).
- 2+ years of experience with workflow management frameworks (e.g., Apache Airflow, Luigi, Kubeflow).
- 1+ years of experience with open-source data search and storage systems such as Elasticsearch and/or Apache Accumulo.
- 1+ years of experience with agile development, including knowledge of CI/CD toolsets (e.g., Gitlab).
Python (Programming Language)