Principal AI Engineer - Software

Key Responsibilities

• Design and implement scalable agent-driven systems to automate Computer Vision MLOps workflows (training, deployment, evaluation)
• Develop robust APIs and backend services that enable agent orchestration, memory management, and cross-component communication
• Integrate vector databases, data quality validation, and semantic search to power intelligent agent behavior
• Own the end-to-end software lifecycle: from development to testing, CI/CD automation, and cloud deployment
• Build for reliability and maintainability: logging, monitoring, and exception handling are part of your design thinking
• Optimize performance and cost in storage and compute, especially for vision-specific workloads
• Work with researchers and domain experts to translate state-of-the-art ideas into working code

Must-Have Skills

5+ years of hands-on software engineering experience, with at least 23 years building AI/ML systems

Strong Python development skills, including writing testable, production-grade code

Proven experience with:

oDesigning and deploying CI/CD pipelines for ML systems

oContainerization and orchestration (Docker, Kubernetes)

oCloud infrastructure (AWS, GCP, or Azure)

oAPI development using frameworks like FastAPI or Flask

oDatabase technologies: SQL/NoSQL and vector databases (e.g., FAISS, Weaviate)

Familiarity with AI agent frameworks such as LangChain, AutoGen, CrewAI

Solid understanding of Computer Vision workflows, including image classification, detection, and tracking

Comfortable with software engineering best practices: version control, code reviews, documentation, modular design

Nice-to-Have Skills

• Demonstrated experience through personal projects, prototypes, or open-source contributions in AI, CV, or agents
• Hands-on with ML frameworks like PyTorch or TensorFlow
• Exposure to ML observability and performance monitoring tools (e.g., Grafana, Datadog, Prometheus)
• Experience building simple user-facing interfaces for demos or internal tools (e.g., with Gradio or Streamlit)
• Familiarity with unit testing frameworks (e.g., Pytest) and static analysis tools
• Prior experience in cross-functional collaboration, especially with researchers or product engineers
• Knowledge of LLM integration for autonomous agent systems