We are seeking a talented Software Engineer to support our complex AI knowledge system that leverages cutting-edge technologies such as GPTs and vector databases. The ideal candidate will have a strong background in developing scalable, distributed systems and microservices, with a keen interest in AI and machine learning. This role is both a leadership and hands-on role that will involve collaborating with a dynamic team of AI/ML engineers, data scientists, and software developers to build, deploy, and maintain innovative AI solutions. You have experience that demonstrates building a software engineering team, shipping code at an early stage startup and rolling up your sleeves to build ML software products and services.
Responsibilities:
AI Product Support: Lead and support the development and enhancement of AI products, ensuring seamless integration and functionality for end-users.
Data Pipeline Development: Design, build, and maintain efficient data pipelines to support machine learning platforms, enabling data scientists to develop and deploy models effectively.
Technical Leadership: Provide technical ownership in AI and machine learning projects, making key technical decisions and overseeing project organization and milestones.
Team Building: Recruit, hire, and grow a high-performing engineering team. Foster a collaborative and innovative culture within the engineering department.
Team Management: Lead, mentor, and collaborate with engineers and data scientists, facilitating effective communication and teamwork across diverse functions.
Stakeholder Engagement: Engage with product management, field operations, and the VP of ML & Engineering to identify and address AI product requirements. Bridge communication gaps and implement cohesive plans to align product requirements with technical implementation.
Project Implementation: Hands-on implementation of critical components, including REST APIs, data transformation processes, and backend-to-frontend data integrations.
Process Improvement: Review and refine existing processes to enhance team efficiency, communication, and the integration of QA and documentation in the planning stages.
Qualifications:
Experience: Proven track record as an software engineering leader in AI, machine learning, or data-intensive environments. Experience supporting and enhancing the collaboration of machine learning engineers and data scientists in a fast-paced setting.
Technical Skills: Proficient in Java, Python, Groovy, Ruby, GraphQL, SQL. Familiar with middleware technologies such as Quarkus, Kubernetes, Docker, AWS, Spring Boot, REST, and others. Strong experience in developing and debugging Python applications. Django and Django Ninja
Expertise: Strong experience with Django and Django Ninja frameworks, including RESTful API design and implementation. Familiarity with Pydantic and async support.
Database Knowledge: Experience with PostgreSQL, S3, Vector and Graph Databases, Elasticsearch, and Redis. Collaborate with AI/ML engineers and researchers to implement CI/CD pipelines that support reproducible experiments and safe deployments.
Tools Proficiency: Skilled in using development, build, and deployment tools like Git, Maven, Ant, Gradle, CircleCI, Docker, Artifactory, and Jenkins.
Leadership: Demonstrated ability to build and grow a high-performing engineering team, lead cross-functional teams, mentor junior engineers, and manage project timelines and deliverables.
Strategic Thinking: Strong strategic decision-making skills with the ability to set a vision for the engineering team and align it with overall company goals.
Market Awareness: Ability to research the market, identify key trends, and adapt strategies to stay ahead in the competitive AI and machine learning landscape.
Communication: Strong communication skills with the ability to engage and align diverse stakeholders.
Problem Solving: Excellent problem-solving skills with a strategic approach to addressing complex technical challenges that meet the needs of an early stage startup company's needs.
Collaboration and Code Reviews: Participate in software design reviews and peer code reviews. Provide constructive feedback to team members and collaborate on implementations.