Principal AI/ML Engineer — Production Systems

Fidelity; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Fidelity seeks a Principal AI/ML Engineer to grow Fidelity Institutional by building scalable AI solutions and deploying production-ready systems for lead generation, intelligent product recommendations, and client engagement.

You will design LLM/RAG architectures, develop data foundations, APIs, and cloud AI infrastructure, and partner with business teams to deliver measurable outcomes with strong monitoring and MLOps.

Qualifications

• Bachelor's or Master's degree in CS/Engineering/Data Science or related field.
• 6+ years of software or AI/ML engineering experience.
• Hands-on experience deploying AI solutions in production.
• Proficiency in Python and building maintainable production apps.
• Experience designing APIs and integrating AI into enterprise workflows.
• Experience with cloud-native AI solutions and MLOps practices.

Responsibilities

• Develop production AI and ML solutions using LLMs, RAG, and agentic AI frameworks.
• Build scalable data foundations, APIs, and cloud-based AI infrastructure.
• Create reliable, secure, observable systems with a production-first mindset.
• Collaborate with business and technology teams to translate challenges into solutions.
• Establish monitoring, governance, and MLOps processes for performance and scale.
• Evaluate emerging technologies and apply them to deliver measurable business impact.

Skills

AI/ML production

Python

API design

Cloud deployment

LLMs & RAG

LangChain/LangGraph

MLOps

Communication

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

AWS

Snowflake

dbt

Airflow

Job description

Fidelity seeks a Principal AI/ML Engineer to grow Fidelity Institutional by building scalable AI solutions and deploying production-ready systems for lead generation, intelligent product recommendations, and client engagement.

You will design LLM/RAG architectures, develop data foundations, APIs, and cloud AI infrastructure, and partner with business teams to deliver measurable outcomes with strong monitoring and MLOps.