Principal Machine Learning Engineer (Hybrid)

RTX•East Hartford, CT

2d•Hybrid

About The Position

Pratt & Whitney is seeking a Principal Machine Learning Engineer to join the Engineering AI Team within the Digital Engineering Organization. The team is tasked with accelerating end-to-end AI/ML solutions across our value stream (from Engine Design and Development, Production, and Sustainment), supporting enabling AI platforms, and building our Digital Discipline. This role involves developing and integrating AI/ML solutions into existing and future domain-specific systems, including physics-informed regressions, machine vision, and language models. The engineer will build, test, deploy, and monitor AI systems across multiple use cases such as design optimization, product inspection, field investigation, and productivity assistants. A key aspect of the role is developing and maintaining AI and ML models throughout their lifecycle, from data gathering and feature engineering to model training, validation, deployment, and monitoring. Additionally, the role requires developing and maintaining full-stack software systems that integrate AI models and capabilities, and architecting, evaluating, implementing, and maintaining elements of the P&W AI Platform Ecosystem (e.g., MLOps, Image Annotation, Databricks Workspaces). The position also supports the efficient rollout of enterprise-wide AI productivity tools like Microsoft Copilot and involves defining, documenting, and training AI/ML best practices.

Requirements

Bachelor's degree in Science, Technology, Engineering or Mathematics (STEM) and 8 years of relevant digital and/or engineering experience; or Advanced Degree in a related field and 5 years of relevant digital and/or engineering work experience
3 years of relevant work experience with Data and AI or otherwise Digital-driven Engineering applications and a combination of the following: production AI/ML modeling, pipelines, system integration and model monitoring
Development and deployment of software systems that integrate one or more AI components
Hands on experience using LLM-based chat systems (ChatGPT, Gemini, Copilot, etc.)

Nice To Haves

Active Secret Clearance
Professional Certificates for AI and Cloud Applications (e.g., AWS Solution Architect Associate or Profession, AWS ML Specialty, etc.)
Prior knowledge of the aerospace industry (design, analysis, manufacture, and/or aftermarket support)
Experience developing, deploying, and maintaining a production cloud-based application (Amazon Web Services or Microsoft Azure)
Experience deploying and managing ML on edge devices
Experience developing, deploying, and maintaining Python-based packages or web/API applications
Experience implementing various types of AI solutions including, LLM/GenAI, Machine Vision, Physics Informed Regressions, etc.
Experience with ML packages such as Tensorflow or PyTorch
Experience managing AI Platforms (Databricks, mlflow server, AWS Sagemaker Studio, or other internal solutions)
Experience deploying and managing production ML (including MLOps) and software systems
Engineering experience with multi-disciplinary analysis and optimization (MDAO) experience with FEA or CFD and optimization
Disciplined software engineering experience (e.g., automated testing, code reviews, CI/CD)
Navigating compliance processes for export control, legal, cyber security, architectural review, etc.

Responsibilities

Developing and integrate AI / ML solutions into existing and future domain specific systems.
Build, test, deploy, and monitor AI systems across multiple use cases (design optimization, product inspection, field investigation, productivity assistants, etc.)
Develop and maintain AI and ML models across their lifecycle - from data gathering, feature engineering, model training, validation, deployment, and monitoring
Develop and maintain full stack software systems that integrate AI models and capabilities
Architect, evaluate, implement, and maintain elements of the P&W AI Platform Ecosystem (e.g., MLOps, Image Annotation, Databricks Workspaces, etc.)
Support efficient rollout of enterprise wide AI productivity tools such as Microsoft Copilot
Define, document, and train AI/ML best practices