[Hiring] AI Architect @Enlyte

Role Description

This is a full-time remote position that can be located anywhere in the U.S. We are seeking an experienced AI Architect to lead the design, development, and governance of enterprise-grade AI and Agentic AI solutions and standards across the organization. This role sits at the intersection of cloud-native AI engineering, data architecture, and AI governance — shaping how the company builds, deploys, and scales intelligent systems.

The ideal candidate brings deep hands-on expertise with the AWS AI/ML stack, a strong foundation in data governance and data architecture, and a forward-looking vision for agentic AI enablement. You will be a strategic technical leader who can translate business objectives into scalable AI architectures while ensuring responsible, governed, and secure AI practices.

Key Responsibilities

• AI Architecture & Strategy
  • Define and own the enterprise AI reference architecture, including patterns for model training, inference, orchestration, and agentic workflows.
  • Evaluate, select, and integrate AWS AI/ML services into a cohesive, scalable platform strategy.
  • Architect end-to-end AI solutions — from data ingestion and feature engineering through model deployment, monitoring, and feedback loops.
  • Lead the technical design of Agentic AI systems, including multi-agent orchestration, tool-use patterns, retrieval-augmented generation (RAG), and autonomous decision-making workflows.
• AWS AI Stack Expertise
  • Serve as the subject matter expert on the AWS AI ecosystem, including but not limited to:
    • Amazon SageMaker — model training, fine-tuning, hosting, endpoints, Pipelines, Feature Store, Model Registry, and Ground Truth.
    • Amazon Bedrock — foundation model access, custom model import, Guardrails, Knowledge Bases, and Agents.
    • AWS Agent Core — agentic runtime orchestration, tool integration, and session management.
    • Amazon Q — enterprise AI assistant capabilities, customization, and integration.
    • Amazon Kendra / OpenSearch — enterprise search, semantic retrieval, and RAG pipelines.
    • AWS Step Functions & EventBridge — workflow orchestration for AI pipelines and event-driven architectures.
    • Amazon Comprehend, Textract, Rekognition, Transcribe, Polly — applied AI services for NLP, document processing, vision, and speech.
    • AWS Lambda, ECS/EKS, App Runner — serverless and containerized inference hosting.
    • Amazon CloudWatch, SageMaker Model Monitor — observability, drift detection, and model performance tracking.
• Data Governance & Data Architecture for AI
  • Design and enforce data governance frameworks that ensure data quality, lineage, cataloging, and compliance across AI workloads.
  • Architect data pipelines and storage strategies optimized for AI/ML development (feature stores, data lakes, lakehouses).
  • Collaborate with data engineering teams to establish robust data contracts, schemas, and metadata standards.
  • Ensure AI training data and inference data meet regulatory, ethical, and privacy requirements (e.g., PII handling, bias detection, GDPR/CCPA alignment).
  • Leverage AWS data governance tools including AWS Glue Data Catalog, AWS Lake Formation, Amazon DataZone, and AWS CloudTrail for audit and lineage.
• Agentic AI Enablement
  • Design frameworks and patterns for building, deploying, and managing autonomous AI agents at enterprise scale.
  • Define standards for agent tool-use, memory management, guardrails, human-in-the-loop escalation, and multi-agent collaboration.
  • Establish evaluation and testing frameworks for agentic systems (accuracy, safety, latency, cost).
  • Partner with product and engineering teams to identify and prioritize agentic AI use cases.
• Leadership & Collaboration
  • Mentor engineers and data scientists on AI architecture best practices and AWS tooling.
  • Collaborate with security, compliance, and legal teams to embed responsible AI principles into the development lifecycle.
  • Present architectural recommendations and roadmaps to senior leadership and stakeholders.
  • Stay current on emerging AI technologies, foundation models, and AWS service launches; evaluate their applicability to enterprise needs.

Qualifications

• Bachelor’s Degree in Computer Science, Software Engineering, or related field.
• 8+ years of experience in software architecture, data architecture, or AI/ML engineering, with at least 3 years in a senior or lead architect role.
• Deep, hands-on expertise with the AWS AI/ML stack (SageMaker, Bedrock, Agent Core, Kendra, Comprehend, etc.).
• Strong foundation in data architecture — data modeling, ETL/ELT pipelines, data lakes, lakehouses, and cloud-native data platforms.
• Proven experience in data governance — data quality frameworks, metadata management, data cataloging, lineage tracking, and compliance.
• Experience designing and deploying large language model (LLM) solutions, including prompt engineering, fine-tuning, RAG, and embedding strategies.
• Demonstrated experience with agentic AI patterns — multi-agent systems, tool orchestration, autonomous workflows, and guardrail design.
• Strong understanding of MLOps practices — CI/CD for ML, model versioning, A/B testing, monitoring, and retraining pipelines.
• Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar).
• Experience with infrastructure-as-code (CloudFormation, CDK, Terraform) for AI/ML resource provisioning.
• Excellent communication skills with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

• AWS certifications — Solutions Architect Professional, Machine Learning Specialty, or Data Analytics Specialty.
• Experience with Amazon DataZone, AWS Lake Formation, and AWS Glue for governed data sharing and cataloging.
• Familiarity with graph databases (Neptune, Neo4j) for knowledge graph–driven AI applications.
• Experience building multi-modal AI solutions (text, image, video, audio).
• Background in insurance, healthcare, or regulated industries where data governance and compliance are paramount.
• Experience with cost optimization strategies for AI workloads at scale.

Benefits

• Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts.
• Life and AD&D Insurance.
• 401(k).
• Tuition Reimbursement.
• An array of resources that encourage a lifetime of healthier living.
• Compensation depends on the applicable US geographic market. The expected base pay for this position ranges from $133,000 - $180,000 annually, based on a number of additional factors including skills, experience, and education.