Machine Learning Engineer - Large Language Models

Key Skills

Azure, Data Analysis, Data cleaning / Data cleansing, Data pipelines, Data science techniques, Data Visualization, Design, Large Language Models - LLMs, Linear Algebra, Machine learning techniques, Python Programming, PyTorch, Snowflake, SQL, Streaming APIs

Job Description

POSITION OVERVIEW

At Kiewit, we operate in seven different markets, with many dynamics that impact business decisions. We thrive on being the best at what we do, and to continue our success, we must navigate the future with confidence, embracing decision-making with clear, concise, data-driven analysis, tools & solutions that deliver value to each of our business areas. Our Data Science team helps our executives and leaders advance their businesses through effective analysis, machine learning, and AI tools to support our Engineering and Construction projects.

As a Machine Learning Engineer, you will team up with business stakeholders, and innovative engineers to prioritize innovation ideas and work with other data scientists and engineers, to develop and deliver breakthrough Machine Learning solutions, predictive models and visualizations. This work will transform how we perform Engineering analysis and project design for all of our market areas.

DISTRICT OVERVIEW

Kiewit Data Services’ (KDS) mission is to make Kiewit the premier data-driven organization in our industry. To accomplish this, our projects and districts need to have the right data, of the right quality, with the right level of analysis, available to them at the right time. KDS is a cross functional organization with employees that have expertise in data, technology, support and operations backgrounds. Our core functions are data Quality, Governance, Enablement, Analytics and Data Science.

LOCATION

This position is based in the office at our headquarters, in Omaha Nebraska. This is not a remote work opportunity. We do offer relocation assistance.

RESPONSIBILITIES

• Training and deploying Large Language Models end to end. This could include conduct problem formation, data cleaning, data analysis, predictive modelling, and continuous improvement for machine learning opportunities in the engineering and construction space.

• Present model results and their applications to team members and stakeholders
• Adhere to software engineering best practices for machine learning architecture, development, and maintenance.
• Develop and deploy model APIs to Azure and Snowflake.
• Collaborate with data engineering to design data pipelines for data science projects. Work with application developers to provide model predictions for web applications.
• Create training materials and provide in-person trainings to department managers and engineers.

QUALIFICATIONS

• Interest in employing machine learning, primarily LLMs, to address opportunities in the engineering and construction industries.

• BA / BS in Analytics, Statistics, Mathematics, Computer Science, Economics, or related field; with a demonstrated interest in Large Language Models. Candidates holding advanced degrees (master’s or Ph.D.) in these disciplines are also encouraged to apply.
• Must have at least one year of experience training and deploying LLMs end to end.
• 2+ years Python programming required.
• Proficiency with SQL to support data acquisition functions required.
• Real world experience applying machine learning techniques (Gradient boosting, PyTorch, linear regression, KNN, etc.)
• Ability to effectively communicate key findings or relationships using Python data visualization techniques.

Initial Projects

• Label procurement invoice data by market basket item type

• Summarize company financial data into a tweet-like report for executives to review on a weekly basis.
• Using large language models to clean and augment engineering data.

Other Requirements:

• Regular, reliable attendance
• Work productively and meet deadlines timely
• Communicate and interact effectively and professionally with supervisors, employees, and others individually or in a team environment.
• Perform work safely and effectively. Understand and follow oral and written instructions, including warning signs, equipment use, and other policies.
• Work during normal operating hours to organize and complete work within given deadlines. Work overtime and weekends as required.