Senior Software Engineer building and maintaining the AI Platform infrastructure. Requires extensive experience with Kubernetes, observability, cloud services, Java/Spring Boot, and daily use of Generative AI coding assistants.
158k – 176k/yr
Remote5+ YOEML Engineering
About the role
Responsibilities
Identify, prioritize and execute tasks in the software development life cycle
Work with business to iterate over software requirements
Develop tools and applications by producing clean, efficient code
Automate tasks through appropriate tools and scripting
Analyze and debug systems
Perform validation and verification testing in a test-driven manner
Review the work of others, and invite others to review your work
Collaborate with internal teams and vendors to fix and improve products
Ensure software is up-to-date with latest technologies
Requirements
Experience writing clean code that performs well at scale using Java (or other functional or object-oriented languages)
Experience with Azure cloud services or equivalent
Experience with cloud native streaming using Azure Event Hub/Service Bus (or others, such as AWS Kinesis, Google Pub/Sub)
In-depth knowledge of relational databases (e.g. Microsoft SQL Server, PostgreSQL)
Experience with GitHub Actions, Jenkins CI/CD pipeline
Experience with Spring Boot
Solid experience writing RESTful API endpoints
Absolutely love TDD and have working knowledge of it
Proficient in GIT
Experience using system and performance monitoring tools (e.g. Azure Log Analytics, Grafana, DataDog)
Experience with automated testing frameworks (e.g. Selenium, Cypress, Jest, Playwright)
Excellent organization, critical-thinking and personal leadership skills
Self-starter with the ability to deliver with minimal supervision
Being okay with the uncomfortable feeling that comes from learning new things
Team player
Analytical mind with problem-solving aptitude
BSc/BA in Computer Science or a related degree
Use of Generative AI Code Assistants (e.g. GitHub Copilot) is a must and working knowledge of spec-driven development. Daily application of the latest Generative AI Model capabilities is a must.
Extensive experience building and maintaining AI platform infrastructure, Kubernetes, and container security.
Demonstrated expertise in observability, and monitoring frameworks, with a focus on real-time performance (i.e: experience with OpenTelemetry, NLPFlow).
Experience with AI infrastructure components such as vector databases, prompt/versioning stores, and AI IDEs.
Nice-to-Haves
Experience with Kafka, or Kafka compatible platforms (e.g. Redpanda, WarpStream, or others)
Experience with integration engines such as Rhapsody, Mirth, or others
Experience with message brokers such as RabbitMQ
Experience with Docker, Kubernetes and Istio
Experience with Ansible
Experience with SAML, OAuth and OpenID Connect
Experience working on a SaaS product
Experience with Service Oriented Architecture
Knowledge of scripting languages such as Python, Bash
Familiarity with vLLM, SGLang or similar framework to host LLM inference workloads.
Experience with CI/CD pipelines and automation for AI model deployment and platform operations
Strong knowledge of authentication and authorization frameworks integrated into AI platforms.
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