W2 (No C2C) :: Remote (USA, EST Zone) :: Senior Backend Engineer Ontology & GenAI; G.C / U.S.C

Senior Backend Engineer – Ontology & GenAI || G.C / U.S.C

12+Months

Remote (USA, EST Zone) 

Eligibility: W2 only – G.C / U.S.C  (No C2C / No Third Parties)

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.