Senior Platform Engineer (AI Platform)

As a
Senior Platform Engineer
, you will help design, build, and evolve Compass Digitals
AI platform for LLM-powered applications and agentic systems
. You will create secure, scalable, production-grade capabilities for orchestration, retrieval, tool integration, evaluation, observability, and governance. Working closely with platform, data, product, security, and engineering teams, you will enable AI copilots, operational automation, and intelligent customer experiences across our digital ecosystem.

Key Responsibilities

- Design and operate the core platform capabilities that power
LLM applications, copilots, and agentic workflows
across multiple environments.
- Architect
single-agent and multi-agent execution patterns
, including tool calling, workflow routing, state management, and human-in-the-loop checkpoints.
- Build and maintain a secure
integration layer
that connects models to internal APIs, data products, and enterprise systems using patterns such as
Model Context Protocol (MCP), OpenAPI-defined tools, and event-driven services
.
- Develop
retrieval and knowledge capabilities
that support grounded responses, including document ingestion, chunking, embeddings, vector search, metadata filtering, reranking, and source attribution.
- Establish
evaluation frameworks
and regression tests for response quality, task success, reliability, and safety; use offline and online evals to continuously improve production performance.
- Implement
guardrails and governance controls
for identity-aware access, PII handling, content safety, prompt and tool security, auditability, and compliance.
- Create end-to-end
observability
for prompts, tool invocations, agent traces, latency, failure analysis, and token or cost usage to support debugging and production operations.
- Automate platform provisioning and deployment using
Terraform, containers, CI/CD, and cloud-native services
.
- Optimize
model selection, throughput, latency, resilience, and cost efficiency
across AI workloads.
- Collaborate with data and ML teams to expose governed structured and unstructured data to AI applications in a safe, reusable way.
- Help define reusable standards, platform patterns, and engineering best practices for building reliable
AI and agent-based systems
at scale.

Qualifications

- Proven experience building or operating
production AI/LLM platforms
, developer platforms, or complex distributed systems.
- Strong hands-on experience with
Python
and API or service development; experience with
TypeScript, Go, or Java
is a plus.
- Experience designing
agentic systems
or advanced LLM applications that use
tool calling, workflow orchestration, retrieval-augmented generation (RAG), and state management
.
- Familiarity with modern agent frameworks and platforms such as
OpenAI Agents SDK, Amazon Bedrock Agents, LangGraph, or similar tooling
.
- Strong understanding of
vector search, embeddings, knowledge base design, ranking/reranking, and grounded generation
.
- Experience with
AWS
and modern platform infrastructure, including containers, serverless services, Kubernetes, networking, and IAM.
- Experience with
Terraform
or similar Infrastructure-as-Code tools and strong
CI/CD
automation practices.
- Understanding of
evaluation, prompt testing, offline benchmarks, and release guardrails
for AI systems.
- Hands-on experience with
observability tooling
for logs, metrics, tracing, and incident response.
- Strong grasp of
security, privacy, and governance
for AI systems, including secrets management, RBAC, data protection, and responsible AI controls.
- Ability to work cross-functionally with product, data, ML, and platform teams and translate emerging AI capabilities into reliable platform services.
- Bachelor's degree or equivalent in
Computer Science, Engineering, or a related field
.

Nice to Have

- Experience building internal developer platforms or self-service tooling for AI teams.
- Experience with real-time inference, streaming workflows, or event-driven architectures.
- Familiarity with data platform concepts such as
dbt, Spark, Apache Iceberg, or data product design
.
- Background in hospitality, retail, or large-scale enterprise environments.