Software Development Engineer – Machine Learning

Role Overview

• Implement AI Platforms: Develop and maintain sophisticated AI platform capabilities, focusing on patterns like tool calling, multi-agent architectures, and human-in-the-loop integrations.
• Build ML Infrastructure: Develop and deploy secure, RESTful web services using Python and Kubernetes.
• Collaborative Engineering: Participate in design reviews and code quality initiatives.
• Translate Requirements: Work with cross-functional teams to turn product requirements into functional technical designs.
• Apply MLOps Standards: Utilize industry-standard practices, including automation, observability, and CI/CD, to deliver high-quality ML solutions.
• Continuous Learning: Stay current with evolving AI/ML technologies and contribute to the team’s collective knowledge.

Requirements

• 5+ years experience in software development engineering including designing, developing, and deploying software solutions.
• 2+ years of experience in Python, with a consistent track record of shipping production code and systems.
• 2+ years of experience building scalable data pipelines and working with large-scale datasets.
• 2+ years of validated experience deploying production services to cloud platforms (e.g., AWS, Azure, GCP) and using containerization technologies (e.g., Docker, Kubernetes) for MLOps.
• Bachelor’s degree in a relevant field such as Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• Solid ability in Algorithmic Thinking to design and implement efficient solutions for agentic system development.
• Expertise in the engineering, deployment, and MLOps of advanced machine learning solutions (e.g., generative models, LLMs, RAG, and AI agents).
• Strong understanding of scalable distributed systems, performance optimization, database technologies (e.g., PostgreSQL, Redis), and robust API development.
• Validated algorithmic thinking and a track proven history designing, implementing, and analyzing efficient algorithms for complex problems.
• Demonstrated ability to build flexible, reusable, and well-documented software components, with comprehensive experience in code testing strategies (unit, integration, end-to-end) in a continuous deployment environment.
• Strong sense of ownership and a proven ability to deliver high-quality, finished products efficiently.
• Excellent communication and collaboration skills, emphasizing team collaboration, knowledge-sharing, and delivering customer impact.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Postgres
• Python
• Redis

Benefits

• Workday Bonus Plan
• Role-specific commission/bonus
• Annual refresh stock grants
• Comprehensive benefits