AI Engineer Sr

Job Description

Senior Ai Engineer

• AI & Agent System Engineering

• Orchestration: Implement intent classification flows, tool routing logic, multi-step reasoning chains, session memory management, and structured output parsing.
• Tool Gateway: Build and maintain the deterministic validation layer checking LLM actions against policy rules before calling live banking APIs.
• RAG Pipelines: Optimize document ingestion, chunking/embedding workflows, vector retrieval, and context injection.
• Guardrails & Testing: Implement "Trust Dial" enforcement in code to determine autonomous vs. user-confirmed actions. Build automated agent evaluation tests to catch regressions and hallucination risks.

• Cloud-Native Service Development

• Scalable Architecture: Build containerized, stateless, horizontally scalable workloads designed to degrade gracefully if upstream banking APIs fail.
• Observability: Implement distributed tracing across agent calls and API hops, structured logging for audits, and metric instrumentation for latency/error rates.
• LLM Integration: Manage token budgets, streaming response handling, model fallback logic, and API error handling.
• Event-Driven Systems: Build idempotent consumers/producers on the Kafka pipeline with sub-second latency targets.

• Banking API Integration & Performance

• API Adapters: Build request transformation, authentication, rate limiting, and retry logic connecting the system to bank middleware.
• Transaction Safety: Implement security controls, token management, duplicate transaction detection, and rollback/compensation logic for agent actions.
• Performance Optimization: Balance LLM cost efficiency against data freshness via strategic caching , and resolve bottlenecks in reasoning chains or DB queries.

Essential Criteria

• Software Engineering: 4+ years of professional experience shipping and operating production systems at scale (not internal tools or prototypes).
• Languages: Strong proficiency in Python and/or TypeScript with an emphasis on clean, testable code.
• Distributed Systems: Solid grasp of concurrency, fault tolerance, and API design (REST/GraphQL) in regulated environments.
• Agentic AI Experience: Hands-on experience building/shipping LLM-based systems, RAG components, tool calling mechanisms, and AI safety features (hallucination detection, prompt injection defenses).
• Testing Rigor: Experience building automated evaluation pipelines to validate non-deterministic AI behavior.
• Cloud Native: Strong experience with containerized workloads, CI/CD pipelines, and cloud environments (AWS, Azure, or GCP).

Desirable Criteria

• Experience with Kubernetes (EKS, AKS) and Infrastructure-as-Code (Terraform, AWS CDK).
• Familiarity with canary deployments, feature flags, and production rollback strategies.
• Background in banking API integration, payment rails, and financial transaction safety (idempotency, audit logging).
• Awareness of compliance frameworks like PCI-DSS or SOC 2.

Remote

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