AI Architect

Job Title: AI Architect

Location 1: Onsite – Dallas, TX

Location 2: Hybrid – Toronto, Canada

Position Type: Full-Time Employment (No C2C)

Base Salary: $90k-100k per annum Bonus Benefits

About the Role:

We’re seeking an AI Architect to lead the end‑to‑end architecture of AI platforms and products—spanning data ingestion, model development, deployment, security, and ongoing operations. You will shape technical strategy for LLM/GenAI capabilities (chat, summarization, RAG, agents), define reference architectures, and coach teams across engineering, data, and product.

Key Responsibilities:

• Architecture & Strategy: Own the AI/ML reference architecture (data, model, serving, observability, governance). Establish patterns for LLM applications, RAG pipelines, agent frameworks, prompt management, and evaluation.
• Solution Design: Translate business outcomes into scalable designs across microservices, APIs, event-driven pipelines, and model endpoints. Create HLD/LLD, sequence diagrams, and data flows.
• MLOps & Delivery: Define CI/CD for ML (feature stores, model registry, automated tests, model promotion, rollback). Instrument model monitoring (latency, drift, bias, hallucinations).
• Security & Compliance: Implement guardrails, PII handling, redaction, role-based access, and auditability. Align with SOC2/GDPR/CCPA where applicable.
• Technical Leadership: Mentor engineers on design patterns (caching, vector search, orchestration, embeddings). Run architecture reviews and build vs. buy assessments.
• Stakeholder Management: Partner with product, security, and data teams; present trade-offs and ROI, create roadmaps and success metrics.

Required Skills & Qualifications:

• Experience: 6–8 years in software/data, with 3 years in AI/ML and at least 2 years designing LLM/GenAI solutions.
• Education: BS/MS in Computer Science, Data/AI, or related field (or equivalent experience).

Technical Expertise:

• LLMs & GenAI: Prompt engineering, retrieval augmentation, fine‑tuning/adapter training (LoRA/PEFT), hallucination mitigation, evaluation.
• Python (FastAPI/Flask), TypeScript (Node optional); building robust AI services.
• Vector databases: Pinecone, Weaviate, FAISS; embeddings (OpenAI, SentenceTransformers).
• Frameworks: LangChain, LlamaIndex; agentic orchestration (task planning, tools).
• Data & Pipelines: ETL/ELT, streaming (Kafka), feature stores; SQL proficiency.
• MLOps: MLflow/W&B, model registries, experiment tracking; Docker/Kubernetes, Helm.
• Cloud: Azure (preferred) or AWS/GCP—managed AI services, storage, networking.
• DevOps: GitHub Actions, Terraform, secrets management, IaC patterns.

Preferred Skills:

• Knowledge of Azure AI, Vertex AI, or SageMaker; API gateway design; serverless (Functions/Lambda).
• Experience with content safety, toxicity filters, and policy enforcement.
• Background in information retrieval, RAG optimization (chunking, metadata, reranking).

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Soft Skills:

• Strong systems thinking, ability to simplify complex trade‑offs, executive communication, and mentoring.