Principal Software Engineer, Applied AI

What You’ll Do

• As part of the Forward Deployed Engineering team, contribute during rapid growth by scaling models, improving platform performance, building backend systems, supporting client-facing deployments, and enabling smoother workflows for machine learning solutions.
• Develop and maintain AI/ML systems: Build robust, scalable backend systems that support machine learning operations and data processing pipelines.
• Cloud operations and management: Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models.
• Problem solving: Independently explore and address complex problem spaces to improve system capabilities and performance without extensive guidance.
• Cross-functional collaboration: Work closely with ML engineers and data scientists to integrate advanced ML technologies, ensuring seamless operations across various platforms.
• Client engagement: Collaborate directly with clients, working embedded with client teams to support use case discovery, product development, and AI deployment.
• Innovation and R&D: Actively participate in research and development of new tools that can enhance AI capabilities and workflows.

What We Need

• 8+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
• Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
• Prior experience working directly with clients in use case discovery, product development, and leading client engagements.
• Technical Expertise:
  • Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
  • Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
  • Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
  • Familiarity with Kubernetes and other container management tools.
  • Ability to write well-structured, organized code and automated unit/E2E tests.
  • Comfortable with polyglot persistence models (SQL vs. NoSQL).
• ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
• Problem solving: Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges.
• Communication: Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.