Join Our Team at Secure Passage

Shape the Future of Security

At Secure Passage, we’re committed to protecting what matters most through innovation, collaboration, and excellence. Our team thrives on tackling complex security challenges with creativity and integrity. Join us and be part of a mission-driven company that values accountability, teamwork, and a passion for securing the future.

Ready to bring your talents to a team that values your contributions and empowers you to grow? Explore career opportunities at Secure Passage and help us shape the future of security.

Open Positions

Software Engineer

Key Responsibilities
• Design, develop and maintain robust Golang and Python-based backend layers that support both our web applications and analytics engines.
• Design, develop and maintain our front-end in React.
• Contribute to story refinement meetings and architecture/design discussions.
• Develop and maintain unit tests across all layers of the system.
• Conduct code review and merge requests.

Qualifications
• 3+ years of experience in software development, ideally with a product-focused company.
• Demonstrable experience with Golang and Python development.
• Experience building applications using a JS framework such as React and React Native.
• Experience with Git and version control systems.
• United States Work Authorization without sponsorship is required.
• Bonus: Strong understanding of AWS and GCP cloud services, including best practices for deployment, monitoring, and security.
• Bonus: Experience with Docker and optimizing containers for performance and resource utilization.

Desired Skills & Competencies
• Experience using document-based and relational databases.
• Experience with AWS, GCP, and Docker.

Site Reliability Engineer

Key Responsibilities
• Kubernetes Expertise: Create, deploy, configure, and manage applications using Kubernetes, with a strong focus on AWS Fargate/EKS and GCP CloudRun. Responsibilities include optimizing deployments for security, stability, and monitoring.
• Cloud Infrastructure Management: Manage AWS or GCP-based infrastructure.You will be responsible for deploying, scaling, and monitoring infrastructure components to ensure availability, resilience, and performance.
• Security Focus: Implement and maintain security best practices, including encryption in transit and at rest. Configure and monitor necessary security components (VPC, IAM, etc.) to ensure data and infrastructure security.
• Container Optimization: Work with development teams to optimize containerized applications, specifically Golang, Python, and Node.js, for performance, scalability, and resource efficiency.
• Monitoring & Stability: Set up and manage monitoring tools (CloudWatch, CloudTrail, Prometheus, Grafana, and Google Cloud Monitoring tools, etc.) to track performance, identify bottlenecks, and maintain overall system health.
• App Load Testing (Bonus): Experience in running load tests to ensure applications can handle expected traffic. Analyze results and recommend optimizations based on performance metrics.

Qualifications
• 3+ years of experience in a Site Reliability Engineer, DevOps, or Infrastructure Engineer role.
• Demonstrable experience with Kubernetes in deployment, optimization, security, and monitoring.
• Strong understanding of cloud services, including best practices for deployment, monitoring, and security.
• Deep knowledge of securing cloud environments and data, with hands-on experience configuring encryption mechanisms.
• Experience with Docker and optimizing Golang, Python and Node.js-based containers for performance and resource utilization.
• Bonus: Knowledge of app load testing techniques and tools.

Desired Skills & Competencies
• Automation: Experience with Infrastructure-as-Code (IaC) tools such as Terraform.
• Collaboration: Work closely with development teams to improve CI/CD pipelines and automate deployment processes.
• Problem Solver: Strong troubleshooting skills with a proactive approach to identifying and resolving infrastructure issues.
• Security Mindset: Consistent focus on security across development and operational practices.

Artificial Intelligence (AI) Engineer

Key Responsibilities
• Design and Development: Architect, build, and maintain scalable and robust AI models and systems. This includes everything from data collection and preprocessing to model training, evaluation, and deployment.
• Model Implementation: Implement and fine-tune machine learning algorithms, including but not limited to, deep learning, natural language processing (NLP), computer vision, and reinforcement learning techniques.
• Production Deployment: Oversee the entire lifecycle of AI models, ensuring seamless integration into production environments and continuous monitoring for performance and accuracy.
• Data Infrastructure: Collaborate with data engineers to design and build efficient data pipelines and infrastructure to support AI and machine learning workflows.
• Algorithm Optimization: Research and implement advanced algorithms and techniques to optimize the performance, scalability, and efficiency of our AI systems.
• Cross-Functional Collaboration: Work closely with data scientists, software developers, product managers, and other stakeholders to understand business requirements and translate them into technical solutions.
• Stay Current: Continuously research and evaluate the latest advancements in AI and machine learning to identify opportunities for innovation and improvement.
• Ethical AI: Champion and implement best practices for responsible and ethical AI development, ensuring fairness, transparency, and accountability in our models.

Technical Skills
• Programming Proficiency: Expert-level knowledge of Python is essential. Familiarity with other languages such as R, Java, or C++ is a plus.
• Machine Learning Frameworks: Hands-on experience with popular machine learning libraries and frameworks like TensorFlow, PyTorch, scikit-learn, and Keras.
• Deep Learning: A strong understanding of deep learning architectures (e.g., CNNs, RNNs, Transformers) and their applications.
• Data Science & MLOps: Proficiency in data manipulation and analysis libraries (e.g., Pandas, NumPy) and experience with MLOps tools and practices for model versioning, CI/CD, and monitoring.
• Big Data Technologies: Experience working with large datasets and distributed computing frameworks such as Apache Spark and Hadoop is highly desirable.
• Cloud Computing: Familiarity with cloud platforms like AWS, Google Cloud Platform, or Microsoft Azure and their AI/ML services.
• Software Engineering Principles: Solid understanding of software development best practices, including version control (Git), testing, and code optimization.

Qualifications
• Education: Although not required A Bachelor's or Master's degree in Computer Science is a plus, Data Science, Artificial Intelligence, or a related technical field.
• Experience: A proven track record of developing and deploying machine learning models in a production environment. The required years of experience will vary depending on the seniority of the role (e.g., Junior, Senior, Lead).
• Problem-Solving: Strong analytical and critical thinking skills with the ability to tackle complex problems.
• Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Collaboration: A team player with a proven ability to work effectively in a collaborative environment.
• Adaptability: A continuous learner who is eager to stay at the forefront of a rapidly evolving field.

Desired, but not Required, Skills
• Experience with generative AI models (e.g., GANs, VAEs, Large Language Models).
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
• Contributions to open-source AI/ML projects.
• Publications in relevant academic conferences or journals.