Principal, Artificial Intelligence Engineer
Principal, Artificial Intelligence Engineering
Role Overview
We are looking for a Principal AI Engineer to serve as the technical lead for our newly established AI Research & Engineering team. This senior individual contributor role requires a highly experienced engineer with deep software engineering expertise, strong technical leadership, and foundational knowledge of AI and large language models (LLMs).
You will play a critical role in shaping and delivering our AI strategy, building production-grade systems, and mentoring junior engineers. This position offers the opportunity to work on cutting-edge technologies while fostering a culture of innovation and continuous learning.
Key Responsibilities
- Collaborate with the Executive Director of AI Engineering to define and execute the organization's AI roadmap.
- Architect and implement scalable systems that integrate LLMs and AI into enterprise processes and infrastructure.
- Design and develop production-ready applications for business-critical use cases.
- Provide technical guidance and mentorship to junior engineers (no direct reports).
- Conduct code and architecture reviews to ensure security, compliance, and production readiness.
- Develop testing and monitoring frameworks for AI systems, including hallucination detection and bias evaluation.
- Establish responsible AI practices and safety guardrails for LLM applications in regulated environments.
- Integrate AI agents into organizational workflows and systems.
- Research emerging technologies and recommend adoption strategies.
- Promote a culture of continuous learning and experimentation in a rapidly evolving AI landscape.
Required Qualifications
- Bachelor's or Master's degree in Computer Science or related technical field.
- 10+ years of software engineering and systems architecture experience with proven technical leadership.
- 5+ years as a senior technical contributor on complex production systems.
- Expert in Python and proficient in SQL.
- Strong system design experience: monolithic and microservice architectures, distributed systems, event-driven architectures, APIs.
- Hands-on experience with data engineering, pipelines, and transformations.
- Foundational knowledge of LLMs and familiarity with leading models (e.g., Gemini, Claude, GPT).
- Understanding of AI risk factors: hallucinations, bias, prompt injection, and data privacy.
- Experience mentoring and reviewing work of junior engineers.
- Excellent communication skills for explaining complex technical concepts.
- Demonstrated passion for AI/LLMs and commitment to continuous learning.
Preferred Qualifications
- Production experience with AI applications or LLM-powered systems.
- Knowledge of RAG architectures and context engineering.
- Familiarity with diverse LLM provider APIs and agent integration.
- Experience experimenting with frontier AI tools and products.
- Cloud technologies: AWS, Docker, Kubernetes, CI/CD.
- Infrastructure as code: Terraform.
- Background in financial market infrastructure or derivatives industry.
- Experience working in regulated environments.
FAQs
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