Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
Algorithms @ 4
CI/CD @ 4
Data Structures @ 4
GenAI
Generative AI
Go @ 6
KubeFlow
Kubernetes @ 7
LLM
MLOps @ 4
Machine Learning
Mathematics @ 4
Python @ 6
Vector Databases
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Bloomberg's Engineering AI department is expanding its team of Machine Learning and Software Engineers building AI-driven search, discovery, and workflow solutions. The department uses technologies including transformers, gradient boosted decision trees, large language models, and dense vector databases to develop accurate, low-latency AI systems for financial information and analytics.
The role focuses on building platforms for generative AI applications and creating a cohesive, integrated, and managed AI development lifecycle for building and maintaining AI systems. The teams use open-source technologies such as Kubernetes, Kubeflow, KServe, Argo, Buildpacks, and other cloud-native MLOps technologies.
Responsibilities
- Architect, build, and diagnose multi-tenant AI platform systems.
- Work with AI application teams to design workflows for continuous model training, inference, and monitoring.
- Work with AI experts to understand workflows, identify and resolve inefficiencies, and inform future platform features.
- Collaborate with open-source communities and AI application teams to build a cohesive MLOps experience.
- Design CI/CD automation frameworks that incorporate regulatory requirements.
- Develop cloud-native deployment patterns for AI systems across environments.
- Troubleshoot and debug user issues.
- Provide operational and user-facing documentation.
Requirements
- 4+ years of experience working with an object-oriented programming language such as Python or Go.
- Experience designing cloud-native, distributed platforms.
- Strong knowledge of Kubernetes, Argo, and container orchestration technologies.
- Previous experience with modern CI/CD tools and GitOps workflows.
- Familiarity with implementing automation for model development lifecycles.
- A proactive mentality and ability to collaborate with peers, stakeholders, and management.
- A degree in Computer Science, Engineering, Mathematics, or a similar field, or equivalent work experience.
- Understanding of computer science fundamentals, including data structures and algorithms.
Benefits
Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) match, life insurance, and wellness programs. The role also includes benefits, bonus, and other total rewards subject to company policies and eligibility.