Senior Specialist - Architecture

Job Summary

Required Qualifications

• Strong software engineering fundamentals and proficiency in Python plus Java, Go, TypeScript (a strong plus)
• Experience of working with Codex
• Proven experience building LLM-powered applications in production, tooling integrations for agents, merchant systems, underwriting platforms, transaction stores, risk engines, CRM case tools, knowledge bases, and workflow engines
• Experience designing distributed systems and APIs (REST, RPC), plus event-driven patterns (Kafka, SQS, Pub/Sub)
• Solid understanding of data engineering basics (SQL, data modeling, feature engineering, data quality)
• Handson knowledge of cloud platforms (AWS, Azure, GCP), containers (Docker), and orchestration (Kubernetes)
• Ability to write clean, testable, secure code, comfortable with code reviews and engineering rigor
• Experience with multiagent systems, planning, verification, and autonomous workflow execution
• Familiarity with vector databases, hybrid search, and knowledge graphs
• Familiarity with model evaluation (offline evals, golden datasets, adversarial testing, regression harnesses, AB testing)

Key Responsibilities

• Agentic AI System Design Engineering: Design and implement agent architectures, planners, executors, tools using agents, multiagent orchestration, reflection, evaluation loops. Build tooling integrations for agents, merchant systems, underwriting platforms, transaction stores, risk engines, CRM case tools, knowledge bases, and workflow engines. Implement robust state management, session memory, task plans, provenance traceability, and replay ability of agent actions.
• LLM RAG Engineering for Payments Workloads: Develop RAG pipelines over policies, SOPs, card network rules, underwriting guidelines, dispute playbooks, and merchant agreements. Apply prompt and system design, structured output patterns, and schema validation for deterministic agent behavior. Optimize for latency, cost, and reliability using caching, model routing, and evaluation-driven prompt iteration.
• ML Decisioning Integration: Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, rules engines). Build feedback loops from outcomes, chargeback win rate, false positives, approval uplift to continuously improve models and agent strategies.
• Safety Compliance and Responsible AI: Implement guardrails, PII handling policy enforcement, prompt injection defenses, tool per missioning, rate limiting, and safe failover. Ensure auditability, why an agent took action, evidence used, and human approval where required, human-in-the-loop.
• Productionization MLOps LLMOps: Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics, task success, tool errors, hallucination indicators.
• Collaboration Leadership: Partner with Product, Risk, Ops, Underwriting, Compliance, and Engineering to convert business problems into deployable AI solutions. Mentor engineers, set standards for agent design patterns, testing, and production readiness.

Technical Skills

• Agent frameworks, Lang, Graph, Semantic Kernel, similar orchestration frameworks or equivalent custom implementations
• RAG tooling, embedding pipelines, hybrid retrieval, reranking, chunking strategies, citation provenance, Observability, Open Telemetry, structured logging, dashboards, ing, Data systems (OLTP, analytics warehouses, lakes, streaming pipelines, feature stores), optional Testing (unit, integration tests for tools, replay tests for agent traces, eval harnesses for LLM outputs)

Benefits/Perks

• Comprehensive Medical Plan
• Covering Medical, Dental, Vision, Short Term and Long-Term Disability Coverage
• 401(k) Plan with Company match
• Life Insurance
• Vacation Time, Sick Leave, Paid Holidays, Paid Paternity and Maternity Leave