What You’ll Bring to The Team:

We are looking for a Senior AI Engineer to design, build, and deploy high-quality AI-powered features. This role focuses on owning end-to-end implementation of AI systems within a product area — from prototyping to production — with a strong emphasis on reliability, iteration, and measurable impact. You will work closely with product and engineering teams to turn ambiguous problems into effective AI solutions, while contributing to best practices and raising the bar for AI development.

You Will:

End-to-End AI Feature Ownership

• Design and implement AI-powered features (LLM workflows, copilots, and agent-based systems with tool use and multi-step reasoning)
• Own the full lifecycle: prototyping → evaluation → production deployment → iteration
• Ensure solutions are reliable, performant, and aligned with product needs

AI System Implementation

• Build and optimize:
• Prompt pipelines for specific use cases
• Retrieval systems (embeddings, chunking, ranking)
• RAG-based workflows where needed
• Iterate on outputs to improve quality, accuracy, and consistency
• Design scalable and cost-efficient AI architectures for production workloads
• Select and evaluate models (hosted vs open-source) based on use case constraints
• Agent-Based Systems (AgentCore)
• Design and build agentic workflows capable of multi-step reasoning and decision-making
• Integrate agents with tools, APIs, and internal systems to perform real-world actions
• Implement planning, execution, and reflection loops for complex tasks
• Manage context, memory, and state across multi-step interactions
• Balance deterministic workflows vs. agent autonomy for reliability and control

Experimentation & Evaluation

• Run structured experiments to compare approaches (prompting, retrieval, models)
• Define and track key metrics for AI performance (quality, latency, cost)
• Debug and improve non-deterministic system behavior
• Build and maintain evaluation datasets and benchmarks
• Implement automated evaluation pipelines for continuous improvement

Collaboration & Contribution

• Drive technical direction and influence AI adoption across teams
• Partner with product managers and designers to scope AI features
• Contribute to shared patterns and reusable components
• Participate in code reviews and design discussions
• Support and mentor mid-level engineers where needed

AI Reliability, Safety & Governance

• Design guardrails to ensure safe and reliable AI behavior
• Mitigate hallucinations, prompt injection, and model misuse
• Ensure compliance with data privacy and enterprise requirements
• Implement monitoring and observability for AI systems in production
• Implement guardrails for agent actions (tool access control, execution boundaries)
• Prevent failure cascades in multi-step agent workflows

An Ideal Candidate Has: 

Core AI Skills

• Strong understanding of LLM capabilities and limitations
• Experience with prompt engineering and structured output design
• Hands-on experience with embeddings and vector search
• Familiarity with RAG architectures and when to apply them
• Experience designing agent-based architectures (AgentCore concepts)
• Understanding of tool use, planning strategies, and memory mechanisms in LLM systems

Engineering Skills

• 4+ years of related work experience 
• Solid backend/system design fundamentals
• Evaluate agent performance across multi-step tasks (task success rate, error propagation)
• Debug and optimize agent decision-making and tool selection behavior
• Experience building and deploying production-grade systems
• Ability to debug complex issues, including probabilistic outputs
• Comfort working with APIs, pipelines, and data flows

Product Thinking

• Ability to translate user needs into effective AI solutions
• Strong intuition for balancing quality, latency, and cost
• Focus on delivering measurable product impact

Collaboration

• Communicates clearly across engineering and product teams
• Contributes to team knowledge and shared practices

What Success Looks Like:

• Ships high-quality AI features that deliver clear user value
• Demonstrates strong ownership from idea to production
• Improves systems through structured iteration and experimentation
• Contributes to team-level best practices and reusable solutions

What You’ll Bring to the Team:

We are seeking a Site Reliability Engineer (SRE) to join one of our Scrum teams and help ensure the reliability, scalability, and performance of the Florence™ platform. AI-driven tooling and automation are a cornerstone of how we build, operate, and scale our systems.

In this role, you will work closely with product engineers while actively leveraging AI to improve observability, incident response, automation, and overall platform reliability. Coding assignments in this role will require working with AI-assisted development workflows as a core part of how solutions are designed and delivered.

You Will:

• Be an embedded member of a Scrum team, participating in planning, refinement, reviews, and retrospectives
• Use AI-powered tools to enhance system reliability, operational efficiency, and developer productivity
• Design, build, and operate reliable, scalable cloud infrastructure supporting platform and product services
• Apply AI-assisted analysis to monitoring, alerting, and observability data to detect, predict, and prevent incidents
• Define and maintain SLOs, SLIs, and error budgets to guide reliability decisions
  Collaborate with software engineers to embed reliability and AI-driven automation into the software development lifecycle
• Lead and participate in incident response, root cause analysis, and postmortems, leveraging AI insights where appropriate
• Automate operational tasks and reduce toil through AI-enabled and traditional automation approaches
• Contribute to disaster recovery planning, testing, and operational readiness
• Produce and maintain documentation such as runbooks, operational guides, and system diagrams
• Contribute code as a secondary responsibility, with coding assignments focused on building reliability tooling, automation, and integrations using AI-assisted development practices
  
  

An Ideal Candidate Is / Has:

• Passionate about building reliable, scalable systems using modern, AI-enabled approaches
• Strong understanding of cloud-native and distributed system architectures
• Experience applying SRE principles in a production environment
• Hands-on experience with cloud platforms (AWS preferred)
• Experience using AI-assisted tools for coding, debugging, automation, or operational analysis
• Strong background in Linux, networking, and system operations
• Experience with infrastructure-as-code and automation tools (e.g., Terraform, CI/CD pipelines)
• Familiarity with modern observability practices (metrics, logs, tracing), including AI-enhanced analysis
• Comfortable working as part of an agile, cross-functional Scrum team
• Strong problem-solving, communication, and collaboration skills
• 4+ years of experience in SRE, DevOps, or similar roles
• Experience supporting production systems at scale