AI Engineer
Indexed description
Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.
What You'll Do
- Build containerized AI services in Python. Implement clean APIs where needed and standards-based integrations for enterprise systems.
- Design retrieval & agent flows using industry-standard frameworks; implement prompt/tool versioning and safe rollouts (e.g., feature flags, canary).
- Guardrails & governance: help implement controls around PII handling, audit logging, RBAC, prompt-injection defenses, and egress controls.
- Evaluation automation: create eval harnesses, golden sets, regression gates, and basic business KPIs (e.g., quality, safety, latency, cost).
- Observability: instrument tracing/metrics/logging with standard tooling, integrate with enterprise monitoring/logging platforms, and build actionable dashboards/alerts.
- Operational rigor: contribute to runbooks and incident hygiene. Participate in the on-call rotation for the AI services you help own.
- CI/CD: use pipeline-as-code for delivery and keep code-quality/security gates clean for frequent deployments.
- Team play: embed with asset teams when appropriate. Contribute back reusable components, SDKs, and docs to the AI engineering platform.
- At least 2 years of software engineering experience, including at least 1 production-deployed GenAI use case for real business users or consumers.
- Strong Python and microservice fundamentals (e.g., FastAPI or similar, type hints, tests such as pytest) with an emphasis on well-structured, readable code.
- Hands-on experience with any AI orchestration frameworks (e.g., LangChain, LangGraph, OpenAI Agents SDK, PydanticAI or similar).
- Containers/orchestration experience: solid containerization understanding and hands-on with deploy/scale/config/secret management (e.g., Docker, Kubernetes/OpenShift).
- Observability experience: metrics, logs, tracing (e.g., OTel) and using these signals to debug production outages and performance issues.
- CI/CD discipline (e.g., Azure DevOps YAML or similar), code-quality/security gates (e.g., SonarQube, Snyk), and dependency management basics.
- Governance understanding: audit logs, RBAC, data-privacy boundaries, and change control in business-critical environments.
- Experience deploying and supporting multiple custom GenAI use cases in production.
- Familiarity with MCP, A2A, or other AI integration standards.
- Experience with RAG and vector search.
- Experience with Python dependency/build management (e.g., uv) and familiarity with ASGI servers (e.g., uvicorn).
Behavioral Competencies
- Collaborates
- Customer focus
- Communicates effectively
- Decision quality
- Nimble learning
Equal Opportunity Employer
United States: All applicants receive consideration for employment without regard to race, color, sex, religion, national origin, age, sexual orientation, gender identity, disability, or status as a protected veteran.United Kingdom: Westfield is committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships.
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