AI Tech Architect

Overview

Own the end-to-end architecture of production AI systems with a strong hands-on bias. You’ll design robust, cost-efficient, and secure agentic / GenAI solutions on AWS. Part of your job will be to unblock lead developers by debugging code, optimizing performance, and guiding best practices. Expect to turn complex requirements into scalable, observable, and well-governed platforms.

Responsibilities

• Define target architectures for agentic systems (planning / reasoning / tool-calling), GenAI / RAG pipelines, and evaluation loops; produce clear design documents with Flow / UML / sequence diagrams and AWS deployment topologies.
• Size and optimize infrastructure for cost and performance : model throughput / latency, concurrency, autoscaling policies, CPU / GPU needs, memory footprints, vector index sizing, storage / egress, and token budgets.
• Lead deep-dive debugging and incident resolution : profile bottlenecks, fix defects, stabilize services; pair-program with developers to raise the engineering bar.
• Establish reference implementations for multi-agent frameworks (Semantic Kernel preferred; LangGraph / AutoGen / CrewAI acceptable), tool / function schemas, validation, memory, grounding, and multi-step planning.
• Architect retrieval and hybrid search systems : ingestion, chunking, embeddings, ranking, caching, freshness, and grounding; evaluate recall, precision, and hallucination risk.
• Productionize on AWS using Amazon EKS, S3, SQS / SNS, and AWS Bedrock; integrate identity (Okta / IAM), secrets (AWS Secrets Manager), eventing, and observability; enforce SLIs / SLOs and error budgets.
• Make systems observable : distributed tracing, metrics, and logs using OpenTelemetry and Datadog; standardize dashboards, alerts, and tool / trace replay.
• Build evaluation and promotion workflows : prompt / flow tests, golden sets, offline batch runs, A / B experiments, regression suites, and rollout gates.
• Design security and safety controls : threat modeling, prompt-injection defense, sandboxed tools, policy enforcement, red-team testing, PII / data governance, and audit trails.
• Define platform standards : reusable SDKs, connectors, CI / CD templates, runbooks, and architecture review checklists.
• Partner with product, data, and SRE teams to plan capacity, disaster recovery, multi-region posture, and upgrade paths; lead readiness reviews and post-incident RCAs.
• Mentor engineers and review PRs with a focus on reliability, correctness, and maintainability.

Must Have

Good to Have

Core Tech Stack (our core; equivalents welcome)