Celara

Full-stack Engineer

• Own the technical architecture — backend, frontend, infrastructure, and AI integration
• Write code daily — build the critical systems yourself
• Make stack and design decisions that will shape the product for years
• Set the standard for code quality, testing, and engineering discipline
• Integrate LLMs and AI workflows deeply into the platform
• Work directly with founders on product direction and technical roadmap
• Create clarity and structure in an environment that doesn't have perfect specs
• Mentor and guide the other developers technically

Tech Lead – Full Stack, AI

QA Engineer

• Design, implement, and maintain automated API and service-level test cases using Karate and Postman.
• Collaborate with QA and engineering teams to define test strategies for new features and services.
• Develop and maintain regression and smoke test suites to validate service health and system stability.
• Perform manual exploratory and validation testing for complex backend workflows or non-automatable areas.
• Execute and design backend performance validations using tools like JMeter, k6, or equivalent.
• Validate data integrity and perform root cause analysis using logs, service metrics, and backend databases.
• Run tests in Dockerized environments or local dev environments; contribute to test environment setup and configuration.
• Participate in Agile ceremonies, sprint planning, and cross-functional collaboration.
• Contribute to continuous integration (CI) and continuous testing (CT) pipelines as part of the QA automation lifecycle.

Senior QA Engineer

• Build and enhance systems that allow team members to more effectively engage with the tools in our data ecosystem.
• Attend stand-ups and team rituals, and work with other software engineers to deliver on team goals
• Participate in code reviews with other members of the team
• Be part of an on-call rotation to support our live products
• Provide meaningful code reviews for other members of the team
• Work collaboratively with non technical customers to accomplish deliverables

Software Engineer – Data Foundations

Backend Engineer

• Build and maintain C++ based Streaming Video Players to run on Smart TV, Set Top Box, or Gaming Console platforms.
• Build video player plugins using GStreamer
• Research and evaluate potential technical solutions
• Make design decisions on video player architecture
• Enable Microsoft PlayReady, Google Widevine DRM protection for video player plugins
• Optimize video playback performance, time-to-first-frame (TTFF)
• Maintain technical documentation
• Work and collaborate in teams with engineers, testers, product managers, etc.
• Work in an Agile, iterative development process
• Committing to deliver timely results

Senior C++ Playback Engineer

Artificial Intelligence

• Design, train, evaluate, and deploy machine learning models and deep learning architectures for a range of business problems.
• Build systems to manage, enrich, and filter prompts, including handling sensitive content and ensuring context is maintained for optimal model performance.
• Implement processes to screen and filter model outputs, ensuring responses are free from harmful or inappropriate content.
• Develop APIs and supporting systems to serve AI models to client applications, integrating with both internal systems and third-party platforms.
• Implement rigorous data protection, masking, and encryption strategies to safeguard sensitive information and support compliance requirements.
• Integrate and support workflows leveraging the Model Context Protocol for contextual management of AI model requests and outputs.
• Optimize AI service delivery for scalability, reliability, and low latency, using modern deployment and caching strategies.
• Collaborate with security teams to enforce robust access controls, data privacy, and compliance across all AI solutions.
• Apply logging, monitoring, and tracing for model performance, operational analytics, and cost management.
• Work with ML engineers, software developers, product managers, and DevOps teams to integrate AI capabilities into the larger application landscape.
• Create comprehensive documentation, guides, and internal tooling to support the adoption and maintenance of AI systems.

AI Gateway Engineer – Platform Integrations

Machine Learning Engineer

• Design, train, and deploy machine learning models for production use cases.
• Develop data pipelines for preprocessing, feature engineering, and model serving.
• Implement MLOps best practices: CI/CD for ML, monitoring, retraining workflows, and model lifecycle management.
• Build and validate ML Proof of Concepts (POCs) for new business opportunities.
• Optimize models for performance, scalability, and efficiency (latency, memory, throughput).
• Collaborate with data engineers, DevOps, and product teams to ensure seamless integration of ML systems.
• Stay up to date with new ML/AI research and tools to bring innovation into projects.

Security Engineer

• Implement and enhance secure code review practices with tools like SonarQube and Semgrep
• Automate SAST/DAST scanning in CI/CD pipelines for services, APIs, and containers
• Manage open-source library risk: SBOM generation, dependency scanning, CVE and license tracking
• Partner with developers to remediate findings and embed security into code review and release workflows
• Support penetration testing efforts and coordinate remediation of web, API, and business logic vulnerabilities
• Work with Cloud Security Posture Management to improve security controls across AWS, including ECS/ECS-Anywhere
• Own configuration and optimization of AWS security services (GuardDuty, Security Hub, Config, CloudTrail)
• Take ownership of encryption strategies using AWS KMS, certificate management, and secrets management
• Secure ECS and ECS-Anywhere container deployments with runtime protection and monitoring
• Implement container image scanning and vulnerability management workflows
• Design security frameworks for Lambda functions and serverless architectures
• Build security automation for container and function lifecycle management
• Contribute to the development and security of AI infrastructure (AWS Bedrock, Lambda, agentic frameworks, MCPs)
• Implement prompt hardening, secrets protection, and access controls for AI-powered services
• Develop security monitoring and response strategies for AI agents and workloads
• Ensure application and cloud environments meet SOC 2, PCI, and ISO compliance requirements
• Implement automated compliance monitoring, drift detection, and reporting
• Design and maintain security baselines and configuration standards
• Create compliance evidence collection workflows aligned to developer pipelines

Security Engineer – Cloud Application Security

AI Engineer

• Move fast and don’t break things: ship quickly while keeping quality and reliability high
• Write real software: clean, production-grade code you’re proud of — not just notebooks
• Build AI-powered applications end-to-end: backend, frontend, integrations, and cloud
• Owner mindset: do what it takes – coding, debugging infra, iterating on product etc.
• Iterate fast: ship, gather feedback, improve, repeat
• Thrive in ambiguity: figure out requirements on the fly; create structure where needed
• Own delivery: success is measured by products shipped and how well they perform in the real world
• Work hard alongside founders and teammates — this role demands intensity, long hours, and high standards