Principal AI/ML Engineer

Fidelity•Durham, NC

9d•Hybrid

About The Position

Position Description : Designs, develops, tests, deploys, maintains , and enhances data science Machine Learning (ML) models by building Artificial Intelligence/Machine Learning (AI/ML) pipelines. Develops and tests products and applications to support cloud integrations and incorporate AI/ML and Large Language Model (LLM) technologies. Applies technical tools and methodologies to enable efficiency, performance and agility by automating DevOps processes, Continuous Integration and Delivery (CI/CD) pipelines. Designs, develops, and implements Application Programming Interfaces. Creates and maintains cloud-based data pipeline infrastructure and API applications. Improves Unit Test Automation coverage to achieve the Automation Pyramid. Develops applications supporting data pipelines for feature engineering. Develops vector databases to optimize storage and querying of high-dimensional data, enabling rapid access to relevant information for LLM driven applications .

Requirements

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience as a Principal AI/ML Engineer (or closely related occupation) building large-scale AI/ML pipelines for feature engineering and model deployment, on AWS and Snowflake data warehouses .
Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience as a Principal AI/ML Engineer (or closely related occupation) building large-scale AI/ML pipelines for feature engineering and model deployment, on AWS and Snowflake data warehouses .
Demonstrated Expertise (“DE”) architecting, designing, and building feature engineering pipelines, deploying AI models, and optimizing model inference using PyTorch or TensorFlow
developing ML infrastructure and MLOps in the Cloud using Amazon Web Services (AWS) -- SageMaker, Lambda, Glue, and Step functions
working with predictive and optimization ML models in a development and production environment for deployment, inference, tuning, and required measurements .
DE architecting, designing, and building highly scalable Cloud-based Big Data applications according to business user requirements in AWS, using S3, EMR, Lambda, and Athena
acting as a member of a team responsible for implementing data lake strategies using Snowflake as a platform for structured and semi-structured data
building and formulating data lake design patterns for data ingestion, processing, and extraction for personalization teams using Snowflake, SQL, Python, data warehousing, and advanced data modeling techniques (Entity-Relationship and Dimensional modeling) .
DE architecting, designing, and building Retrieval Augmented Generation (RAG) techniques with vector databases to enhance the capabilities of generative AI \LLMs
developing API frameworks for real-time and near real-time data ingestion (for customer interactions flowing from different channels) using AWS Services - Kinesis (Stream and Firehose) .
DE maintaining Continuous Integration/Continuous Delivery (CI/CD) pipelines for application codes using Jenkins and GitHub
developing Unix shell scripts and creating Control-M jobs to automate and schedule end-to-end processes
performing platform migration by transitioning on-premises systems to AWS cloud infrastructure
ensuring the full potential of cloud-based environments and modern data warehousing technologies (Star Schema or Snowflake Schema) using End-to-End (E2E) migration planning, execution, and optimization .

Responsibilities

Builds and coordinates large-scale ML platforms and tools to enable prediction and optimize models.
Designs and develops a feature generation/store framework that promotes sharing of data/features among different ML models.
Partners with Data Scientists and to help use the foundational platform upon which models can be built and trained.
Operationalizes ML models at scale.
Builds tools to help detect shifts in data/features used by ML models and identifies issues in advance of deteriorating prediction quality.
Monitors the uncertainty of model outputs.
Automates prediction explanations for model diagnostics.
Explores new technology trends to simplify data and ML ecosystem .
Drives innovation and implements solutions with future thinking.
Designs and develops REST-based API solutions.
Delivers system automations by setting up Continuous Integration/Continuous Delivery (CI/CD) pipelines.
Mentors and coaches team members on project activities .