Role Overview
Design, deliver, and operate cloud-native backend platforms that power APIs, orchestration, integrations, and knowledge capabilities.
Combine strong software engineering with AI/agentic development to produce secure, scalable, and resilient services from design through production operations.
What You’ll Do
Build and run backend services, APIs, event-driven workflows, and integration layers on AWS.
Implement domain logic, orchestration, and service composition for core business workstreams.
Integrate with enterprise systems, third-party platforms, data services, pricing/underwriting capabilities, and AI/agent services.
Use AI coding copilots responsibly to accelerate development; enforce strong code review, testing, and release standards.
Establish automated unit, integration, contract, performance, and regression tests; maintain quality gates in CI/CD.
Contribute to API standards, reusable patterns, golden paths, infrastructure as code, and service reliability practices.
Own production readiness, observability, incident response, defect management, and continuous improvement.
Collaborate with Product, Analysts, and Architecture on requirements, contracts, and acceptance criteria.
Model and maintain ontologies, taxonomies, vocabularies, and semantic relationships; build knowledge graphs and metadata structures enabling cross-domain reasoning.
Engineer LLM/agent patterns including RAG pipelines, prompt strategies, orchestration flows, evaluation frameworks, and reusable AI components.
Minimum Qualifications
5+ years designing and operating backend systems in Java, Python, .
NET, Node.js/TypeScript, or similar.
API-first design, microservices, and event-driven architecture in distributed systems.
AWS proficiency (e.g., ECS/EKS/Lambda, API Gateway, S3, DynamoDB/RDS, SQS/SNS/Kinesis, CloudWatch).
Containers, CI/CD (GitHub Actions/GitLab/Jenkins), infrastructure as code (Terraform/CloudFormation), and secrets management.
Strong testing discipline (unit, integration, contract—e.g., Pact), performance tuning, and production support/on-call experience.
Observability and reliability engineering (metrics, tracing, logging, SLOs; OpenTelemetry/PrometheGrafana/ELK).
Security fundamentals: OAuth2/OIDC, JWT, service-to-service auth, KMS/Secrets Manager, least privilege.
Preferred Skills
Ontology and semantic tech: ontology/taxonomy design, RDF/OWL/SHACL or property graphs (Neo4j), SPARQL/Gremlin, metadata and schema governance.
Knowledge graphs, entity modeling, concept mapping, entity resolution, and semantic enrichment.
Generative AI: LLM app development, prompt engineering, function/tool calling, agent orchestration (LangChain, LlamaIndex, Semantic Kernel).
RAG architectures: embedding models, vector databases (pgvector, Pinecone, OpenSearch/OpenAI/Vertex), semantic search/tuning, evaluation frameworks.
Data integration patterns, streaming (Kafka/Kinesis), caching (Redis), and API gateways/service mesh (Kong/Apigee/Istio/App Mesh).
Contract-first development, domain-driven design, and platform engineering patterns that reduce dependencies through strong contracts and self-service.
How You Work
Outcome-oriented engineer who shapes service architecture and owns it through production.
Pragmatic collaborator who clarifies requirements, manages contracts/interfaces, and reduces cross-team coupling.
Comfortable in AI-assisted delivery: you guide, review, harden, and operationalize agent-generated outputs.
Impact Areas
Build the tech and data foundation for the MI Transformation program.
Enable pricing, underwriting, and workspace workstreams with reliable services, APIs, and knowledge capabilities.