Principal AI Architect
American IT Systems•Atlanta, GA
•Remote
About The Position
We are hiring a Principal AI Architect — a deeply technical, hands-on individual contributor who operates at the intersection of AI research, system architecture, and real-world implementation. This is not a management role. It is designed for someone who thrives on building — writing code, designing scalable systems, and staying ahead of the rapidly evolving AI landscape. You will define how AI systems are built across the organization, set architectural direction from first principles, and act as a key bridge between emerging AI innovations and production-ready solutions.
Requirements
- 12+ years in AI/ML engineering, data science, or related fields.
- Proven experience delivering production-grade AI systems.
- Strong experience with LLM applications (RAG, agents, prompt engineering, embeddings).
- Frameworks such as LangChain, LangGraph, AutoGen, CrewAI.
- Experience building Multi-agent systems.
- Knowledge graph-based retrieval (e.g., Neo4j).
- Real-time inference APIs.
- Solid ML foundation: NLP, deep learning, XGBoost.
- Time-series forecasting, causal inference, experimentation.
- Expert in Python and SQL.
- Hands-on with cloud platforms (AWS / GCP).
- Experience with Docker, FastAPI, CI/CD pipelines.
- Familiarity with tools like BigQuery, FAISS, vector databases.
- Experience working in regulated environments (financial services, cybersecurity, healthcare).
- Understanding of compliance frameworks such as GDPR, SOC 2, or SOX.
Nice To Haves
- Master's or PhD in Computer Science, Statistics, or related field.
- AWS ML or GCP ML certifications.
- Experience with enterprise AI platforms (e.g., Snowflake Cortex).
- Background in SaaS, fintech, or ML services organizations.
- Open-source contributions or published technical content.
Responsibilities
- Design and implement end-to-end AI architectures, including: Multi-agent systems and orchestration layers, RAG pipelines and hybrid retrieval (knowledge graphs + vector search), Text-to-SQL systems and real-time inference APIs.
- Own technical blueprints from data ingestion to production deployment and monitoring.
- Solve complex challenges such as: Latency optimization, Precision vs. recall trade-offs, Context window management, Hallucination mitigation, Cost-efficient LLM usage at scale.
- Drive architectural decisions with clear trade-off analysis (build vs. buy, frameworks, deployment models).
- Write production-grade code across the AI lifecycle (Python, SQL, APIs).
- Build reusable AI components: Retrieval layers, Chunking pipelines, Agent tool-calling modules.
- Rapidly prototype and productionize AI solutions with strong observability and evaluation.
- Own CI/CD pipelines, containerization (Docker), and deployment workflows.
- Continuously evaluate the evolving AI ecosystem (LLMs, agent frameworks, retrieval techniques).
- Benchmark models and tools to guide adoption decisions.
- Translate AI trends into actionable product and engineering roadmap inputs.
- Engage with the AI community (research papers, open source, conferences).
- Establish engineering best practices: Code reviews, testing frameworks, and reusable libraries.
- Act as the senior reviewer for AI system design decisions.
- Mentor engineers through pairing sessions and architecture reviews.
- Lead internal training and upskilling initiatives on production AI systems.
- Partner with Product, Data Science, and Platform teams to align architecture with business needs.
- Clearly communicate technical trade-offs to non-technical stakeholders.
- Ensure compliance with governance standards (e.g., SOC 2, GDPR, SOX) in regulated environments.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
