AI Engineer with Agentic AI Exp.

Job Summary:

Required Qualifications

Must Have:

• Strong software engineering fundamentals and proficiency in Python plus Java Go TypeScript is a strong plus
• Experience of working with Codex.
• Proven experience building LLM powered applications in production tool calling function, calling structured outputs retrieval and evaluation.
• 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 and data quality
• Handson knowledge of cloud platforms AWS or Azure or Google Cloud Platform containers, Docker and orchestration Kubernetes preferred.
• Ability to write clean testable secure code comfortable with code reviews and engineering rigor
• Experience with multiagent systems planning verification and autonomous workflow execution
• Experience with vector databases hybrid search and knowledge graphs
• Familiarity with model evaluation offline evals golden datasets adversarial testing regression harnesses and AB testing.

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

Key Responsibilities:

1. Agentic AI System Design Engineering

• Design and implement agent architectures planner executor tool 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

2. 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 behaviour.
• Optimize for latency cost and reliability using caching model routing and evaluation driven prompt iteration.

3. ML Decisioning Integration

• Combine LLM agents with classical ML models fraud scoring anomaly detection risk scoring and rules engines.
• Build feedback loops from outcomes chargeback win rate false positives approval uplift to continuously improve models and agent strategies.

4. Safety Compliance and Responsible AI

• Implement guardrails PII handling policy enforcement prompt injection defences tool per missioning rate limiting and safe failover.
• Ensure auditability why an agent took action evidence used and human approval where required humanintheloop.

5. Productionization MLOps LLMOps

• Build CICD 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

6. 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.

Mandatory Skills: Python AI, Java, LLM, RAG, Agentic AI, ML, Productionization MLOps LLMOps Good to Have Skills: AI/Generative AI, Google Cloud Platform Cloud Architecture & Services