AI Software Engineer at Genia enhancing productivity through AI implementations. Responsible for developing scalable solutions in data, AI, and BI workflows.
About the role
- Software Engineer responsible for technical strategy and AI system integration across clinical trial platforms at Sitero. Collaborating with teams to drive innovation in healthcare technology.
Responsibilities
- Lead technical direction on high-impact projects, designing and maintaining large-scale software solutions that extend across the full stack - from data storage to UI design.
- Drive the design and implementation of state-of-the-art AI solutions and ML infrastructure, ensuring efficient model deployment, evaluation, and fine-tuning within our production environments.
- Serve as a technical leader in the design of scalable data loading and storage solutions, collaborating with research and infrastructure teams to support AI workloads.
- Pioneer the adoption of AI-native development workflows. You will actively use and integrate LLMs to generate boilerplate, refactor legacy code, write comprehensive tests, and optimize development velocity, setting the standard for the wider engineering team.
- Establish alignment and clarity across teams on goals and outcomes while influencing and coaching a distributed team of engineers.
- Apply in-depth knowledge of how AI infrastructure interacts with other systems to maximize training and inference performance, including optimizing hardware accelerators where necessary.
Requirements
- Bachelor's degree in computer science, Engineering, or related field, or equivalent practical experience.
- 8+ years of experience in software development with strong expertise in data structures and algorithms.
- 5+ years of experience in a technical leadership role, leading project teams, setting technical direction, and mentoring engineers across a matrixed organization.
- 7+ years of experience leading technical project strategy involving ML design and optimizing industry-scale ML infrastructure.
- 2+ years of experience with state-of-the-art AI techniques (e.g., LLMs, Multi-Modal models) and concepts.
- Deep familiarity with using AI coding assistants and LLMs to augment daily software engineering tasks (code generation, debugging, architecture review).
- Experience with distributed heterogeneous systems, high-performance computing, or large-scale data management.
Benefits
- Equal opportunity workplace
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