Principal AI Architect

Job Title: Principal AI Architect

Location: REMOTE
Contract

About the Role

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.

Key Responsibilities

AI Architecture & System Design

• 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)

Hands-On Engineering

• 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

AI Strategy & Market Intelligence

• 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)

Technical Leadership & Enablement

• 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

Cross-Functional Collaboration

• 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

Required Qualifications

Experience

• 12 years in AI/ML engineering data science or related fields
• Proven experience delivering production-grade AI systems

Technical Expertise

• 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

Tech Stack

• 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

Domain Experience

• Experience working in regulated environments (financial services cybersecurity healthcare)
• Understanding of compliance frameworks such as GDPR SOC 2 or SOX

Preferred Qualifications

• Masters 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