AI Architect

Enlyte•UNAVAILABLE, UNAVAILABLE

6d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
Serve as the subject matter expert on the AWS AI ecosystem, including but not limited to: Amazon SageMaker, Amazon Bedrock, AWS Agent Core, Amazon Q, Amazon Kendra / OpenSearch, AWS Step Functions & EventBridge, Amazon Comprehend, Textract, Rekognition, Transcribe, Polly, AWS Lambda, ECS/EKS, App Runner, Amazon CloudWatch, SageMaker Model Monitor.
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.
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.
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.