Principal AI/ML Engineer

Fidelity Investments•Westlake, TX

About The Position

The Principal AI/ML Engineer builds and maintains large-scale machine learning infrastructure, data and application pipelines, and ML platforms and evaluates models using Apache Spark and distributed data processing frameworks. The role establishes Python ML ecosystems using object-oriented programming in Java, Scala, and Python, SQL, and Unix scripting. It uses ETL/ELT, messaging and streaming technologies such as AWS SQS, Kinesis, and Kafka, relational and NoSQL databases, APIs, in-memory technologies, open-source tools, and CI/CD tools to develop scalable distributed systems. The position translates divisional initiatives into business solutions by developing complex ML infrastructure, evaluating alternatives, and recommending changes to development policies, procedures, standards, and strategies. It builds analytics and ML platforms for model prediction and optimization, extends platforms for scalable training and deployment, integrates model outputs with business and software engineering teams, and designs data architectures and processing systems aligned with business needs. The engineer performs complex technical and functional analysis across multiple projects, coordinates project schedules and assignments, and is responsible for meeting project goals on time and within budget. The role advises senior management on technical strategy and mentors junior team members. Required expertise includes AWS-based distributed systems and platforms, large-scale data processing and analytics, CI/CD, monitoring and observability, and research and development of reinforcement-learning algorithms and multi-agent simulation systems.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent), and five years of experience as a Principal AI/ML Engineer or in a closely related occupation executing cloud computing functions for distributed systems.
Alternatively, a master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent), and three years of experience as a Principal AI/ML Engineer or in a closely related occupation executing cloud computing functions for distributed systems.
Experience architecting CI/CD pipelines, building large-scale data processing and analytics platforms, and developing advanced monitoring and observability systems in AWS.
Expertise architecting, designing, and building scalable big data frameworks and analytics platforms using Apache Spark and Kubernetes.
Expertise developing and optimizing EMR workloads on EKS for petabyte-scale datasets and improving scalability and performance using AWS services including EMR, EKS, Lambda, CloudWatch, and SQS in a cloud-native environment.
Expertise designing, implementing, and maintaining CI/CD pipelines for distributed systems using Git repositories and AWS CDK, automating deployment workflows and monitoring with AWS CloudWatch, and migrating on-premises platforms to AWS.
Expertise conducting AI and ML research focused on reinforcement learning; developing algorithms using advanced RL methods, including Monte Carlo Tree Search for decision-making in multi-agent systems; and implementing and validating RL models in simulation using Java, Python, and JMonkey Game Engine.
Expertise designing and developing real-time simulation platforms, implementing multi-agent RL models in large-scale simulations using Java-based 3D engines, and evaluating model performance and safety metrics through simulation experiments.

Responsibilities

Build sophisticated analytics, ML platforms, and software to enable model prediction and optimization.
Develop highly scalable distributed systems and software applications using open-source technologies.
Extend existing ML platforms and frameworks to scale model training and deployment.
Partner with business and software engineering teams to drive adoption and integration of model outputs.
Implement big data solutions in the data analytics space.
Plan, create, and maintain data architectures aligned with business requirements.
Build data flow channels and processing systems to ETL and integrate data from various sources.
Confer with systems analysts and software engineers or developers to design systems and obtain information about project limitations, capabilities, performance requirements, and interfaces.
Develop software system testing and validation procedures, programming, and documentation.
Advise senior management on technical strategy.
Mentor junior team members.
Perform independent and complex technical and functional analysis for multiple projects supporting divisional initiatives.
Plan and coordinate complex project schedules and assignments for multiple projects.
Meet project goals on time and within budget.
Architect, design, and build scalable big data frameworks and analytics platforms using Apache Spark and Kubernetes.
Develop and optimize EMR workloads on EKS for petabyte-scale datasets, and improve platform scalability and performance through testing and analysis using AWS services.
Design, implement, and maintain CI/CD pipelines for distributed systems using Git repositories and AWS CDK.
Automate deployment workflows and monitoring using AWS CloudWatch.
Migrate platforms from on-premises systems to AWS Cloud infrastructure while ensuring production scalability and reliability.
Conduct AI and ML research focused on reinforcement learning, develop algorithms using methods such as Monte Carlo Tree Search for multi-agent decision-making, and implement and validate RL models in simulation environments.
Design and develop real-time simulation platforms for testing and validating AI algorithms; implement multi-agent RL models in large-scale simulations and run experiments evaluating performance and safety metrics.