Who you are

• 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+ 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), coupled with a 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

What the job involves

• As a Software Development Engineer for Machine Learning, you will be a key contributor to the team, working closely with senior engineers and ML researchers to build and deploy robust, scalable AI platforms
• You will play a vital role in turning innovative ML concepts into production-ready solutions, with a focus on agentic AI capabilities such as planning, reasoning, and action execution frameworks
• 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. You will write clean, performant code that ensures these systems are resilient in production
• Build ML Infrastructure: Develop and deploy secure, RESTful web services using Python and Kubernetes. You will contribute to the development of multi-tenant runtime architectures that enable fast inference and scale to millions of users
• Collaborative Engineering: Participate in design reviews and code quality initiatives. You will apply software development best practices to ensure our codebase remains maintainable, testable, and efficient
• 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