Principal AI/ML Engineer

Fidelity Investments•Boston, MA

20h•Hybrid

About The Position

Position Description: Performs independent and complex technical and functional analysis for multiple projects supporting several divisional initiatives. Performs deep data analysis on multiple database platforms, using SQL. Supports customer personalization and optimization efforts according to Artificial Intelligence (AI) and Machine Learning (ML) techniques. Designs, implements, and improves technical solutions for AI model deployment, data pipelines, hosting, and Application Programming Interface (API) access. Deploys code and services to Amazon Web Services (AWS) by establishing Continuous Integration/Continuous Deployment (CI/CD) pipelines. Develops APIs and front-end applications, using Java, Spring Boot, Python, or Angular. Extracts and manipulates data pulled from databases or unstructured data sources, by building AWS-based AI solutions.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, Data Science, Analytics, Mathematical Finance, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal AI/ML Engineer (or closely related occupation) performing requirement analysis, design, and development of Cloud-native AI and ML platforms, using information retrieval systems, vector structured and unstructured databases, and production model serving infrastructure in a financial services environment.
Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, Data Science, Analytics, Mathematical Finance, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal AI/ML Engineer (or closely related occupation) performing requirement analysis, design, and development of Cloud-native AI and ML platforms, using information retrieval systems, vector structured and unstructured databases, and production model serving infrastructure in a financial services environment.
Demonstrated Expertise (“DE”) developing retrieval-augmented generation (RAG) systems using Python, FastAPI, Large Language Models (Claude, Bedrock, or Azure OpenAI), vector databases, and AWS OpenSearch
building asynchronous REST APIs with sub-500ms response times using asyncio and Kubernetes
creating interactive user interfaces (UIs) for experimenting with chunking strategies and search algorithms, using Streamlit and Python
performing root cause analysis and debugging, and logging frameworks to mitigate memory leaks and production issues, using Python profilers.
DE architecting AI and ML infrastructure using AWS SageMaker, Lambda, Docker, or Kubernetes
developing REST APIs for Vision-Language Models (VLMs) using FastAPI, and supporting Base64 image-to-tensor transformations
implementing multimodal inference endpoints using SageMaker processing image-text pairs with similarity scoring
establishing cross-Virtual Private Cloud (VPC) connections using PrivateLink for multi-business unit access.
DE building feature store architectures for real-time model serving using AWS SageMaker Feature Store, S3, or Athena
implementing parallel processing frameworks to achieve latency reduction, using Python multiprocessing and concurrent futures
developing data Extract-Transform-Load (ETL) pipelines processing millions of records using AWS Glue, Step Functions, or PySpark
deploying ML models using SageMaker endpoints for real-time inference and SageMaker Processing Jobs for batch inference.
DE establishing secure AWS infrastructures using Identity and Access Management (IAM) roles, Lambda functions, or VPC security groups
implementing Natural Language Processing (NLP) model deployment pipelines for text classification and sentiment analysis, using Hugging Face Transformers, PyTorch, or SageMaker
developing automated data validation and anomaly detection scripts, using Python, pandas, or scikit-learn across multi-year datasets
optimizing SQL queries and model scoring pipelines, using PostgreSQL and AWS Athena.

Responsibilities

Builds sophisticated analytics and ML platforms and tools to enable innovative AI solutions that enhance data accessibility and intelligent automation.
Designs, builds, and automates complex and customized data pipelines with multiple ingress sources and consumers.
Automates manual and error prone processes using AI and ML techniques.
Deploys ML models and operations.
Deploys solutions using software engineering principles.
Deploys solutions using Cloud technologies.
Builds innovative and high business value AI powered solutions to improve customer experiences.
Participates in architecture design discussions and implements application-level architecture.
Develops comprehensive documentation for multiple applications or subsystems.
Establishes full project life cycle plans for complex projects across multiple platforms.
Advises on risk assessment and risk management strategies for projects.
Plans and coordinates project schedules and assignments for multiple projects.
Provides technology solutions to daily issues and technical evaluation estimates on technology initiatives.
Advises senior management on technical strategy.
Mentors junior team members.
Develops original and creative technical solutions to on-going development efforts.