AI/ML Software Engineer

Company Description

The company is an unconventional group of market-leading SaaS software companies, serving industries ranging from behavioral health to transportation to childcare. The company believes deeply in the power of people and data to grow great organizations, and that sharing knowledge, expertise, and resources across its community of businesses drives exponential growth. The company has acquired over 50 businesses since its inception in August of 2016. We are backed by Alpine Investors and operated by world-class PeopleFirstTM leaders.  Founders of leading SaaS companies continue to trust the company to grow their businesses and build even stronger legacies for the future. To learn more, visit [link for members]

We hire and unleash a diverse array of leaders across the company whose will to win - and ability to do so with love - is the true magic behind our success.

Job Description

This role is a hands-on builder for the company’s portfolio companies, focused on developing AI/ML systems and data platforms that unlock new value from data. The AI/ML Engineer will lead and contribute directly to projects such as internal LLM applications, data pipelines, ML-powered product capabilities, and scalable data infrastructure. This person will work closely with operating companies to turn data into practical business outcomes, whether by enabling new products, improving decision-making, or building the technical foundations for AI adoption.

What You Will Do

• Lead high-impact AI/ML and data engineering projects across the company portfolio companies, partnering directly with operating companies to design, build, and deliver solutions

• Build hands-on across AI/ML systems, including data pipelines, model integrations, internal LLM applications, APIs, and supporting infrastructure

• Develop LLM-powered tools and workflows that enhance business operations, internal productivity, and product capabilities

• Partner with portfolio companies to identify opportunities to commercialize data through new products, features, and analytics offerings

• Serve as a technical leader and trusted advisor to engineering and data teams across the portfolio, helping shape practical approaches to AI and data adoption

• Contribute to enablement efforts across the portfolio by sharing practical guidance, examples, and best practices for AI/ML and data engineering

• Support training sessions, workshops, and working sessions that help OpCo teams build stronger capabilities in AI, ML, and data infrastructure

• Help establish repeatable patterns, playbooks, and best practices for building and deploying AI/ML and data solutions across multiple companies

• Collaborate closely with portfolio leadership, product stakeholders, and engineering teams to identify where AI, ML, and better data infrastructure can drive measurable impact

• Own the full delivery lifecycle from scoping through production handoff, including structured knowledge transfer that leaves teams capable of owning and extending what was built

Who You Are

• Demonstrated experience in deploying machine learning, data engineering, and applied AI solutions to solve customer and business challenges at scale

• Familiarity with LLM-based applications, model APIs, retrieval workflows, evaluation approaches, and production deployment patterns

• Have developed production solutions with several (but certainly not all) of the following technologies: Bedrock, Sagemaker, Vertex AI, Glue, Airflow, Spark, Hadoop, Storm, time series databases, Parquet, Iceberg, Redshift, Kafka, Kinesis, Big Query, Cassandra, Open/Elastic Search, dbt, Monte Carlo and Databricks

• Experience developing solutions and analyzing data in Python, R and SQL

• Excellence in data validation — ensuring solutions developed with LLM and ML technologies produce results that are valid and grounded in reality

• Understanding of fine tuning an LLM and strong understanding of inference economics and good judgment about when it’s better to use a frontier model or fine tune a smaller or open source model

• Experience with developing solutions to reliably perform forecasting of various metrics (such as: sales projections, budget forecasts, resource consumption, demand forecasting, capacity planning, etc)

• Experience translating business problems and data opportunities into practical AI/ML solutions

• Knowledge of modern data architecture and best practices across ingestion, transformation, governance, quality, and access

• Strong judgment around model performance, system reliability, security, and maintainability in production environments

• Curiosity and discipline to stay current with the latest advancements in AI tooling, LLMs, and applied ML systems

• Strong communication skills, with the ability to work effectively with engineering, product, data, and business stakeholders

• Comfortable supporting cross-company enablement through knowledge sharing, collaboration, and practical guidance

• Comfortable switching contexts quickly and operating effectively across multiple companies, teams, and priorities

Base Salary Range

The target salary range for this position is ($200,000 – $250,000), and is part of a competitive total rewards package including an annual bonus, employer-paid benefits, L&D stipend and incentive pay for eligible roles. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually at minimum to ensure competitive and fair pay.

A Secure Candidate Experience

All official emails and messages regarding opportunities at the company, LLC, will come from our alpinesg.com email domain. Please be wary of communications from similar domains that may contain misspellings or slight variations. These could be attempts at phishing or impersonation. The company will never ask you for sensitive personal information during the hiring process such as social security numbers, banking information or other personal details.