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About This Role
AI Engineer
Country United States of America State / County Oregon City Clackamas Division Structurals Company SSBO Function Engineering Workplace Type On-Site Position type Full Time
Opportunity ID 24724
Job Description The AI Engineer is responsible for designing, developing, and deploying AI-driven workflows and applications to address complex business and engineering problems. The role will primarily leverage Databricks Apps and the PCC data platform, using common Python or Node.JS frameworks such as Streamlit, Dash, and Gradio. This role combines strong software engineering fundamentals with hands-on AI/ML expertise helping teams build intelligent systems that streamline operations, automate analysis, and enhance decision-making. You will collaborate with data scientists, analysts, production teams, engineering, and business leaders to take AI solutions from early concepts and prototypes through production deployment and measurable business outcomes. This is a hands-on role from someone who understands how AI systems behave, fail and how to engineer reliable solutions.
Primary Duties and Responsibilities
- Design and build AI-powered workflows (“AI Agents”) and apps using Databricks.
- Build AI agents and intelligent workflows that reason, use tools, interact with enterprise applications and data, and execute complex multi-step processes with appropriate human oversight.
- Translate business requirements into technical designs, user stories, and functional specifications for assigned workstream. Shape how models learn, reason, and perform with real world business input.
- Evaluate data patterns and system behavior to inform solution improvements and implementation decisions.
- Evaluate and select models and solution approaches based on quality, reliability, latency, cost, security, scalability, and business requirements, and develop systematic evaluation cases to measure solution performance.
- Integrate APIs, model endpoints, and data services into user-facing tools and operational systems.
- Write, review and refining code in collaboration with data scientists to productionize models and embed them into functional business apps.
- Ensure performance, reliability, and scalability of AI workflows in cloud environments.
- Contribute to enterprise AI governance and compliance frameworks (e.g., model lineage, permissions, auditability)
Experience and Education
- 2+ years’ experience in software development, application engineering, or full-stack development
- Bachelor’s or Master’s degree in computer science, software engineering, or a related field (or equivalent experience)
- Preferred experience building web-based or data-driven applications in cloud environments (AWS, Azure, or GCP)
- Experience integrating APIs and developing UI/UX for data and AI-enabled tools
- Preferred certifications: AWS Developer Associate, or Azure App Developer
Required Skills
- Proficiency in Python and Javascript
- Proficiency building applications using common Python or Node.js frameworks (Streamlit, Dash, Gradio, Shiny, Flask, React, Angular, etc.)
- Familiarity with AI/ML concepts and ability to integrate model outputs into apps or workflows
- Prefer AWS AI/ML experience such as Bedrock and SageMaker and/or Azure tools like Azure Cognitive Services/Foundry
- Prefer experience developing Databricks Apps or similar modular, cloud-hosted applications
- Strong understanding of RESTful APIs, microservices, and event-driven architectures
- Proficiency with CI/CD pipelines, Git, and agile software delivery
- Familiarity with ETL pipelines and data modeling frameworks
- Experience with SQL and cloud databases (e.g., Snowflake, Redshift, or Databricks SQL)
- Strong focus on usability, code quality, and maintainability
- Good understand of data algorithms, data structures, and performance tuning.
- Excellent collaboration and communication skills, with a proactive, product-focused mindset
Travel
- Occasional travel to support sites, attend team meetings, and for training.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. National Security and Export Controls:
This position requires use of information or access to production processes subject to national security controls under U.S. export control laws and regulations (including, but not limited to the International Traffic in Arms Regulations (ITAR) and the Export Administration Regulations (EAR)). To comply with those regulations, this position may require applicants to be U.S. Persons (i.e., U.S. citizens, U.S. lawful permanent residents, protected individuals as defined by 8 U.S.C. 1324b(a)(3)), or eligible to obtain the required export authorizations from the U.S. Department of State or the U.S. Department of Commerce. About PCC With more than 120 operations and approximately 20,000 employees worldwide, Precision Castparts Corp. is the market leader in manufacturing large, complex structural investment castings, airfoil castings, forged components, aerostructures and highly engineered, critical fasteners for aerospace applications. In addition, we are the leading producer of airfoil castings for the industrial gas turbine market. We also manufacture extruded seamless pipe, fittings, and forgings for power generation and oil & gas applications; commercial and military airframe aerostructures; and metal alloys and other materials for the casting and forging industries. With such critical applications, we insist on quality and dependability – not just in the materials and products we make, but in the people we recruit.
PCC is relentless in its dedication to being a high-quality, low-cost and on-time producer; delivering the highest value to its customers while continually pursuing strategic, profitable growth.
In 2016, Berkshire Hathaway, led by Chairman and CEO Warren E. Buffett, acquired Precision Castparts Corp.
Precision Castparts Corp. and its affiliates do not accept unsolicited resumes from search firms or employment agencies. Unsolicited resumes will become the property of Precision Castparts Corp & its affiliates, and no fee will be paid.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Precision CastParts, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Precision CastParts AI Hiring
Precision CastParts has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Jenkintown, PA, US, Clackamas, OR, US, US. Compensation range: $168K - $168K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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