Python Engineer (AI Support)

at  Colossyan

ABOUT US

At Colossyan, we are building the future of AI video; we exist to ‘make knowledge transfer easy’. Our platform empowers content creators to transform text into training videos, delivering cost/time savings and enhanced results for over 2000 companies including industry leaders such as Paramount, Novartis, and Continental.
In early 2024, we raised $22million in our Series A funding round, led by Lakestar (known for partnering with Spotify and Revolut among other businesses), to help fuel our next round of sustainable growth! We’re also recognized as a “Leader” on G2.
We take great pride in fostering a culture of growth within our rapidly-growing, international team of over 70. Our organization thrives on a deep-seated passion for technology and its incredible potential, with a strong commitment to the personal and professional development of each team member. Yet, we don’t stop at work – we also value the importance of enjoying ourselves! Our company’s offsite events are a true highlight, offering a fantastic opportunity to connect and bond with colleagues.
Role
As a Python Engineer at Colossyan, you will play a crucial role in developing our infrastructure and applying software engineering best practices around AI model development. This is a role that sits at the intersection of machine learning, software engineering, and data science, offering you the unique opportunity to impact every stage of the lifecycle of an AI model.

QUALIFICATIONS

• Bachelor’s degree in STEM field
• 2+ years of Python engineering experience
• Solid foundations of software engineering best practices
• Experience in maintaining CICD pipelines (Github Actions)
• Knowledge of AWS cloud infrastructure and services (EKS, IAM, S3)
• Excellent communication skills and ability to collaborate with cross-functional teams
• Strong attention to detail and ability to work independently
• Experience in Machine Learning (Argo Workflows)

• Managing datasets:
• Gather, clean, and validating datasets
• Design, develop and maintain data pipelines for asset handling
• Managing the full lifecycle of ML/DL models:
• Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system
• Monitor the model performance and cost-effectivity
• Building ML pipelines:
• Design, build, and maintain efficient, reusable, and reliable pipelines in Python
• Implement and design APIs for both internal and external users to expose the ML models
• Collaboration and Leadership:
• Collaborate with cross-functional teams including full-stack engineers, backend developers, platform engineers, product managers, and ML research engineers to deliver high-quality products
• Quality Assurance:
• Uphold high standards of quality by writing clean, understandable, and testable code
• Enhance and maintain the testing infrastructure to avoid regressions
• Innovation and Research:
• Stay abreast of emerging technologies and trends in machine learning
• Experiment with new technologies and techniques to improve the development process
• Be curious about infrastructure and frameworks of managing AI model lifecycle
• Code Review and Integration:
• Conduct code reviews to ensure adherence to coding standards and to foster a culture of collective code ownership.
• Propose and drive improvements to the whole AI model development lifecycle
• Ownership
• Lead projects
• Be proactive about change, and biased towards action