DS
US_East | Data Engineer_L2
Datum Software, Inc
🇮🇱 Israel
On-site
5 days ago
- AI
- CI/CD
- RAG
- Model Context Protocol
- MCP
- Disaster Recovery
- Load Testing
- Java
- Python
- C#
- .NET
- React.js
- Angular
- TypeScript
- NoSQL
- Kubernetes
- Secrets Management
- Apigee
- Vector Search
5 days ago
Location: Alpharetta / F2F is a must
Job Description
We are seeking a hands-on Platform Engineer with strong full-stack software development and AI engineering experience to join the CPS PEArch team.
The PEArch team designs, builds, and governs scalable, secure, and resilient platform capabilities that support critical Wealth Management applications. The team's mission is to translate architectural standards into reusable engineering solutions, platform services, AI agents, and automation that application teams can adopt at scale.
This is a software engineering role with architecture responsibilities, not solely an infrastructure administration or architecture-review position.
The selected candidate will own solutions throughout the entire lifecycle—from design and coding through testing, deployment, observability, and production support. The candidate will collaborate with Core Platform Engineering, application development, embedded SRE, cybersecurity, infrastructure, and production management teams to improve reliability, accelerate delivery, and strengthen engineering standards.
Key Responsibilities:
Full-Stack Software & Platform Development
- Design, develop, test, and operate production-grade APIs, backend services, web applications, and developer-facing platforms.
- Build self-service portals, infrastructure platform catalogs, reusable components, and automation that simplify adoption of approved platform capabilities.
- Develop integrations across application services, databases, messaging platforms, API gateways, and enterprise engineering tools.
- Write maintainable, secure, and well-tested code.
- Participate in code reviews, technical design discussions, troubleshooting, and performance optimization.
- Own capabilities throughout the SDLC, including CI/CD, deployment, rollback, documentation, and operational readiness.
- Build AI agents and AI-assisted workflows for architecture analysis, engineering knowledge discovery, evidence collection, standards validation, and remediation planning.
- Develop Retrieval-Augmented Generation (RAG) solutions grounded in authorized architecture documents, code, configuration, and platform knowledge.
- Integrate AI agents with enterprise APIs and tools through controlled interfaces, including Model Context Protocol (MCP) where appropriate.
- Implement evaluation frameworks for accuracy, groundedness, safety, consistency, latency, and cost.
- Engineer production controls for AI systems, including access restrictions, bounded execution, auditability, human approval, and safe fallback behavior.
- Automate architecture-review feedback and remediation tracking within developer workflows.
- Identify and remediate single points of failure, shared-resource risks, scaling bottlenecks, and critical dependency weaknesses.
- Implement timeouts, bounded retries, circuit breakers, bulkheads, idempotency, backpressure, and graceful degradation where appropriate.
- Design and validate high-availability, failover, disaster-recovery, and data-reconciliation capabilities aligned with business requirements.
- Build observability through metrics, structured logs, distributed tracing, dashboards, and actionable alerts.
- Partner with SRE and application teams on load testing, failure testing, incident analysis, and recovery validation.
- Translate architectural patterns and standards into reusable libraries, reference implementations, automated checks, and engineering guardrails.
- Develop and maintain infrastructure platform catalogs, adoption guidance, and architecture decision records.
- Detect deviations from approved designs and service-level objectives and help teams prioritize corrective actions.
- Promote consistent API design, secure integration, platform reuse, and measurable production-readiness criteria.
- Share engineering practices through demonstrations, documentation, mentoring, and PEArch Guild sessions.
- Strong hands-on software development experience using Java, Python, C#/.NET, or comparable backend technologies.
- Experience developing modern user interfaces using React, Angular, TypeScript, or equivalent frameworks.
- Demonstrated ownership of production applications or platform services from design through operational support.
- Experience designing APIs, distributed systems, asynchronous integrations, and relational or NoSQL data access.
- Practical experience developing LLM-enabled applications, AI agents, retrieval workflows, or AI-powered automation.
- Experience with containers, Kubernetes or equivalent platforms, CI/CD, and infrastructure/configuration as code.
- Strong understanding of authentication, authorization, secrets management, secure coding, and data protection.
- Ability to diagnose application, database, network, dependency, performance, and reliability issues.
- Strong collaboration and communication skills with the ability to explain technical decisions and trade-offs to diverse stakeholders.
- Experience in Wealth Management, banking, trading, or other high-availability transaction-processing environments.
- Familiarity with API gateways such as Apigee, messaging platforms such as Kafka or MQ, and distributed caching.
- Experience with MCP integrations, agent orchestration, vector search, AI evaluation, and production AI monitoring.
- Knowledge of multi-data-center architecture, disaster recovery, SLOs, and resilience testing.
- Experience building internal developer platforms, service catalogs, policy-as-code, or automated architecture-governance capabilities.
“All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.”
US_East | Data Engineer_L2 · Datum Software, Inc