Site Reliability Engineer II

Job description

Job Title : Site Reliability Engineer II

Job Description

About your role:

We are seeking a highly skilled Technical Lead for AI Development to drive the architecture, design, and execution of advanced AI systems using LLM frameworks, multi-agent architectures, RAG pipelines, and Model Context Protocol (MCP) integrations.

The ideal candidate has strong hands-on experience building production-grade AI features, orchestrating agent ecosystems, evaluating model performance, and iterating through continual refinements.

You will lead a team of engineers, collaborate with product and research teams, and play a key role in shaping our AI strategy and platform capabilities.

We are looking for a Staff Site Reliability Engineer to help us grow our domain expertise and provide support in a new global region to enable 24x7 development velocity as a global company.

From AWS cloud provisioning as code to improving the developer experience in your working timezone, to acting as a guide to best practices around building and delivering software globally, we need an SRE with the passion, motivation, and great ideas to make everything better.

What you’ll do

• Automate the provisioning of all of Juniper Square’s infrastructure in code.
  
  Everything we do is in code!
• Partner with our Platform Engineering team on building developer tooling / improving developer experiences via joint initiatives and enhancements.
  
  
• Partner with our Data Engineering team on improving our data posture and driving operational excellence.
  
  
• Evolve our deployment pipelines to automate infrastructure deployments with the latest and greatest (and reliable) technologies.
  
  
• Improve metrics on our main services, and act as a subject matter expert for our global dev teams.
  
  
• Enable observability, SLO/SLI reporting, and respond to business impacting incidents as it pertains to infrastructure.
  
  
• Adopt and drive solutions that align with AWS Well Architected frameworks and Juniper Square’s business objectives.
  
  
• Identify performance bottlenecks and provide recommendations for improvement.
  
  
• Proactively identify and solve problems that we didn’t even know we had.
  
  
• Help build, deploy, and scale a load testing environment that is analogous to production.
  
  
• Enforce security and operational safety controls.
  
  
• Participate in technical roadmap planning and estimation.
  
  
• Participate and contribute in production readiness and architecture review board (ARB) meetings and forums.
  
  
• Train and mentor future engineers in the same region.
  
  
• Contribute to the architectural improvements to meet future scaling and observability requirements

Qualifications

• A profound love for solving hard problems and overcoming challenging obstacles.
  
  
• Putting your customers first, whether they be internal or external, and making them more productive, happy, and successful.
  
  
• Experience with AWS.
  
  Other public cloud providers are a bonus.
  
  
• Experience with PostgreSQL is a must.
  
  Additional experience with document databases is a nice-to-have.
  
  
• Experience with cloud security best practices (CSPM, CDR, CWPP, SIEM, etc) to keep our customers and cloud posture secure.
  
  
• Experience with containers (builds, registries, vulnerabilities scanning, run-time with docker-compose, run-time with TILT, run-time in schedulers/orchestration systems).
  
  
• Multi-year hands-on experience and fluency with Kubernetes and helm charts are an absolute skill requirement.
  
  We live and breathe the k8s ecosystem.
  
  
• Experience with a CI/CD pipeline.
  
  We use a combination of Github Actions, ArgoCD, Helm and GitOps in our deployment process, but again, any are fine.
  
  
• Some sort of infrastructure-as-code system: Ansible, Terraform, CloudFormation, CDK, etc.
  
  
• We use Python and Typescript, so knowledge and exposure with either is a strong plus.
  
  
• Experience breaking up monolithic architectures into microservices
• Experience with service meshes and service discovery solutions.
  
  
• Experience with an observability solution: New Relic, Prometheus, DataDog, etc.
  
  
• Experience with logging systems: CloudWatch, ELK, Splunk, etc.
  
  
• Bachelor’s degree in Computer Science or similar or equivalent experience

Key Responsibilities:

AI Architecture & Development

• Design and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns.
  
  
• Build scalable RAG (Retrieval-Augmented Generation) architectures using vector databases, embedding pipelines, and data chunking strategies.
  
  
• Integrate and extend MCP (Model Context Protocol) tools for robust model-tool communication and workflow automation.
  
  
• Lead development of AI-based features, prototypes, and production solutions using LLM APIs or self-hosted models.
  
  
• Architect and optimize prompt engineering, prompt chains, agent loops, and refinement pipelines.
  
  

Model Evaluation & Continuous Improvement

• Implement and maintain agent evaluation frameworks (agent evals, scenario tests, regression testing).
  
  
• Design automated evaluation harnesses for LLM quality, reliability, hallucination control, and performance metrics.
  
  
• Drive iterative improvements through A/B testing, reward models, and feedback loops.
  
  
• Monitor system performance, latency, cost, and reliability — and implement optimization strategies.
  
  

Technical Leadership

• Lead and mentor engineers working on AI, data, and backend components.
  
  
• Collaborate with product managers, researchers, and cross-functional teams to align tech strategy with business outcomes.
  
  
• Conduct code reviews, enforce best practices, and maintain architectural standards.
  
  
• Own technical roadmaps, sprint planning, and engineering execution.
  
  

Systems & Infrastructure

• Work with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services.
  
  
• Integrate vector databases (Pinecone, Weaviate, Elasticsearch, etc.).
  
  
• Build APIs and microservices to expose AI capabilities to internal and external stakeholders.
  
  
• Maintain secure, compliant, and efficient data pipelines for ingestion and retrieval.
  
  

Qualifications

• Bachelor’s/Master’s degree in Computer Science, Engineering, AI, or related field.
  
  
• 8+ years of software engineering experience with strong backend architecture skills.
  
  
• 3+ years deep experience with LLMs, GPT models, agents, or advanced ML systems.
  
  
• Strong hands-on experience with:
• MCP tools and LLM tool integration
• Agent frameworks (e.g., OpenAI Agents, LangChain, LlamaIndex, custom agents)
• RAG pipelines, embedding models, vector stores
• Agent evaluation, reliability testing, and model refinements
• Proficiency in Python, TypeScript/Node.js, or similar languages.
  
  
• Experience deploying LLM apps and APIs in production environments.
  
  
• Deep understanding of AI limitations, hallucination control, and safety measures.
  
  

Preferred / Nice to Have

Experience with:

• Fine-tuning LLMs
• OpenAI API, Claude, or Azure OpenAI
• Distributed embeddings and high-throughput retrieval systems
• MLOps frameworks
• Knowledge of DevOps, CI/CD, containerization (Docker/Kubernetes).
  
  
• Prior leadership experience managing small to mid-size engineering teams

Required Skill Profession

Computer Occupations