ASG - AI/ML Software Engineer
Requirements
• 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
Responsibilities
• Lead high-impact AI/ML and data engineering projects across ASG 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
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