Principal, AI / ML Engineer

Fidelity•Westlake, TX
•Onsite

About The Position

Fidelity is seeking a Principal AI/ML Engineer to contribute to the growth of its Fidelity Institutional (FI) business by developing innovative and scalable AI solutions. This role involves creating and deploying production-ready AI systems designed to enhance lead generation, provide intelligent product recommendations, optimize coverage strategies, and improve client engagement.

Requirements

  • Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, Data Science, or a related field.
  • 6+ years of software or AI/ML engineering experience, including building and deploying AI solutions in production environments.
  • Practical experience with LLMs, prompt and context engineering, RAG architectures, and agentic AI frameworks.
  • Advanced proficiency in Python and experience building clean, maintainable, well-tested production applications.
  • Strong experience designing APIs and integrating AI capabilities with enterprise applications, data platforms, and business workflows.
  • Experience deploying cloud-native AI solutions and applying monitoring, observability, governance, and MLOps practices.
  • Experience with AWS, Snowflake, dbt, Airflow, LangChain/LangGraph, or similar technologies.
  • Strong systems-thinking, problem-solving, and communication skills.
  • Ability to balance innovation with practicality, delivering scalable solutions that drive business outcomes.

Nice To Haves

  • Production-first attitude focused on reliability, scalability, maintainability, and user experience.
  • Ability to assess technical decisions through the lens of business impact, operational complexity, and long-term sustainability.
  • Proven success collaborating across technical and non-technical teams in fast-paced environments.

Responsibilities

  • Develop and implement production AI and machine learning solutions utilizing large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI frameworks.
  • Develop scalable data foundations, APIs, and cloud-based AI infrastructure.
  • Build reliable, secure, and observable systems with a production-first mindset.
  • Collaborate with business and technology teams to transform complex challenges into impactful solutions.
  • Establish monitoring, governance, and MLOps practices to ensure performance and scalability.
  • Evaluate emerging technologies and apply them to achieve measurable business outcomes.
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