Staff Software Engineer

### Who you are
- This role is ideal for someone passionate about building high-performance systems that enable AI innovation through robust infrastructure, intuitive tooling, and seamless integration of cutting-edge models
- Bachelor’s or Master’s in Computer Science, Engineering, or related field
- 8+ years of experience in back-end software engineering, with proven ability to design and scale distributed systems
- Strong proficiency in Python and Java, with experience in Spring Boot and object-oriented design
- Hands-on experience with AWS cloud services, Docker, and Kubernetes in production environments
- Solid understanding of SQL databases, data modeling, and performance optimization
- Familiarity with microservices architecture, CI/CD pipelines, and infrastructure automation
- Excellent communication and collaboration skills across engineering and product teams
- Experience with large-scale data pipelines (e.g., Apache Spark, Kafka)
- Knowledge of MLOps principles and AI/ML frameworks (TensorFlow, PyTorch)
- Certifications in cloud platforms or container orchestration
- Awareness of AI ethics, data privacy, and security best practices

### What the job involves
- We’re seeking a Staff Software Engineer with deep expertise in back-end development and scalable platform architecture
- You’ll be at the forefront of operationalizing AI—designing services and data systems that empower teams to build, deploy, and iterate on intelligent applications with speed and reliability
- Platform Architecture & Back-End Development:
- Architect and implement microservices-based platforms with a strong focus on scalability, performance, and reliability. Develop core services using Python, Java, and Spring Boot, and ensure seamless integration with AI and data workflows
- Cloud & Containerization:
- Build and deploy applications on AWS leveraging services for compute, storage, and networking. Implement containerized deployments using Docker and Kubernetes to support CI/CD pipelines and automated scaling
- Data Systems & ETL:
- Collaborate with data engineering teams to design and optimize SQL-based data systems, ETL pipelines, and feature workflows that feed AI models at scale
- Reliability & Observability:
- Drive platform reliability through monitoring, logging, and performance tuning. Implement best practices for CI/CD, automated testing, and infrastructure-as-code
- AI Tooling & Integration:
- Build developer tools and abstractions for integrating LLMs and AI models, including orchestration services and prompting frameworks
- Innovation & Scalability:
- Push the boundaries of platform scalability and performance, ensuring systems can handle large-scale data processing and high-throughput workloads

### Benefits
- Premium Health Benefits
- Distributed Team Building
- Learning & Development
- Award Winning Culture