Generative AI Engineer

About the job

Role Overview

AI Systems Architect

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

• 7–10 years in software/AI engineering, including 4+ years in GenAI application development and 2+ years architecting agentic AI systems.
• Expert in

  Python 3.11+

  (asyncio, typing, packaging, profiling, pytest).
• Hands-on experience with

  Semantic Kernel

  ,

  LangGraph

  ,

  AutoGen

  , or

  CrewAI

  .
• Proven delivery of

  GenAI/RAG systems

  on AWS Bedrock or equivalent vector-based platforms (OpenSearch Serverless, Pinecone, Redis).
• Deep understanding of

  AWS ecosystem

  : EKS, Bedrock, S3, SQS/SNS, RDS, ElastiCache, Secrets Manager, IAM/Okta, Kong API Gateway, Datadog.
• Expertise in

  observability and incident management

  using OpenTelemetry and Datadog.
• Strong focus on

  cost, performance, and security engineering

  —FinOps mindset, autoscaling, caching, and policy enforcement.
• Exceptional communication—clear diagrams, ADRs, and peer review practices.

Nice-to-Have Skills

• Multi-agent orchestration (task decomposition, coordinator-worker, graph-based planning).
• Expertise with vector databases (OpenSearch, Pinecone, pgvector, Redis).
• Experience with AI evaluation, guardrails, and rollout gating.
• Familiarity with frontend agent interfaces, secure APIs, and AuthN/Z best practices.
• Exposure to

  policy-as-code

  , multi-tenant architectures, and feature management (Kong, LaunchDarkly, Flipt).
• Experience with CI/CD via GitHub Actions and IaC (Terraform/AWS CloudFormation).