Generative AI Engineer

About the job

Role Overview

As the AI Systems Architect , you’ll own the end-to-end design and delivery of production-grade agentic and Generative AI systems. This is a highly hands-on role requiring deep architectural insight, coding proficiency, and an obsession with performance, scalability, and reliability. You’ll architect secure, cost-efficient AI platforms on AWS, guide developers through complex debugging and optimization, and ensure all systems are observable, governed, and production-ready.

Key Responsibilities

• Architect Production AI Systems : Design robust architectures for agentic systems (planning, reasoning, tool-calling), GenAI / RAG pipelines, and evaluation workflows. Create detailed design documents, including flow / UML / sequence diagrams and AWS deployment topologies.
• Optimize for Cost & Performance : Model throughput, latency, concurrency, autoscaling, CPU / GPU sizing, and vector index performance to ensure scalable, efficient deployments.
• Lead Debugging & Stability Efforts : Conduct deep-dive debugging, fix critical defects, and resolve production incidents; pair-program with developers to improve code quality and performance.
• Standardize Agentic Frameworks : Build reference implementations using Semantic Kernel (preferred), LangGraph, AutoGen, or CrewAI with strong schema validation, grounding, and memory management.
• Engineer Retrieval & Search Systems : Architect hybrid retrieval solutions including ingestion, chunking, embeddings, ranking, caching, and freshness management while minimizing hallucination risk.
• Productionize on AWS : Deploy and manage systems using Amazon EKS, Bedrock, S3, SQS / SNS, RDS, and ElastiCache. Integrate IAM / Okta, Secrets Manager, and Datadog for observability, enforcing SLIs / SLOs and error budgets.
• Implement Observability & Monitoring : Set up distributed tracing, metrics, and logging via OpenTelemetry and Datadog. Standardize dashboards, alerts, and incident response workflows.
• Govern Evaluation & Rollouts : Build test and evaluation frameworks—golden sets, A / B experiments, regression suites, and controlled rollouts—to ensure consistent quality across releases.
• Embed Security & Safety : Enforce least privilege, PII protection, and policy compliance through threat modeling, sandboxed execution, and prompt-injection defense.
• Establish Engineering Standards : Create reusable SDKs, connectors, CI / CD templates, and architecture review checklists to promote consistency across teams.
• Cross-Functional Leadership : Collaborate with product, data, and SRE teams for capacity planning, DR strategies, and post-incident RCA reviews. Mentor engineers to strengthen design and reliability practices.

Must-Have Qualifications

Nice-to-Have Skills