Principal AI Architect (Technical Architecture · AI Strategy · Hands-On Implementation · Market Intelligence)

Role: Principal AI Architect (Technical Architecture · AI Strategy · Hands-On Implementation · Market Intelligence)

Location: USA/Canada

Remote

Contract

Must-Have Experience

• 12+ years of hands-on experience in AI/ML engineering and data science, with significant depth in production system delivery.

• Deep, working expertise in LLM application development: LangChain, LangGraph, tool-calling agents, RAG, prompt engineering, embedding pipelines, and hybrid retrieval.

• Proven track record architecting and shipping multi-agent systems, knowledge graph-powered retrieval (Neo4j or equivalent), and real-time inference APIs.

• Strong ML fundamentals: XGBoost, deep learning, NLP, time-series forecasting, propensity modelling, experimental design, and causal inference.

• Experience delivering AI systems in regulated industries (financial services, cybersecurity, healthcare) with SOX, GDPR, or SOC 2 compliance awareness.

• Expert-level Python and SQL; fluency with GCP, AWS, Docker, FastAPI, BigQuery, FAISS, and CI/CD tooling.

Technical Depth:

• Ability to design hybrid retrieval architectures that balance precision (graph traversal) and semantic recall (vector similarity), with reranking layers — not just off-the-shelf RAG.

• Hands-on experience reducing LLM inference latency in production (e.g., redesigning pipelines from multi-minute to sub-30-second response times).

• Experience building telemetry and evaluation frameworks that measure AI system quality, coding behaviour, and model performance in production.

• Comfort reading AI research papers and quickly assessing whether new techniques are production-ready or hype.

The Right Mindset

• You are a builder first — you are most energised when you are designing systems and writing code, not in meetings.

• You actively follow the AI market: you know what dropped on Hugging Face last week, you have opinions on LangGraph vs. AutoGen, and you can explain why.

• You make decisions with incomplete information, document your reasoning, and course-correct quickly when new data arrives.

• You raise the bar for the engineers around you without gatekeeping — your instinct is to enable, not to bottleneck.

PREFERRED QUALIFICATIONS

• Master's or PhD in Computer Science, Operations Research, Statistics, or a related quantitative field

• AWS Certified Machine Learning Engineer or GCP Professional ML Engineer certification.

• Completion of an AI Strategy or AI Governance programme.

• Prior experience at a data science / ML services firm, enterprise SaaS, or fintech — where you shipped AI to external customers, not just internal tools.

• Hands-on experience with Snowflake Cortex or comparable enterprise LLM deployment platforms.

• Open-source contributions to AI/ML tooling, published technical writing, or conference presentations.