Associate AI/ML Engineer

CleanSlate Technology Group•Austin, TX

12d

About The Position

CleanSlate Technology Group (CSTG) is a technology consulting firm focused on Data, AI, and Cloud Modernization. Its Data and AI Practice partners with clients to build scalable, reliable data foundations across AWS and Azure, including modern data platforms and pipelines that support analytics, machine learning, and AI in production environments. The Associate AI/ML Engineer supports client delivery by completing defined development, testing, and documentation tasks with guidance from experienced engineers. The role contributes to AI and machine learning solutions, including generative AI and agent-based use cases, that are intended for client production systems. The engineer applies established techniques, learns engineering practices through project assignments, code reviews, and feedback, and collaborates with AI/ML, data, and application engineers. They communicate progress, ask questions, raise blockers, and participate in client discussions with support from more experienced consultants. Success involves completing assigned work with increasing independence, incorporating feedback, documenting results, and building reliable engineering habits.

Requirements

Foundational hands-on experience building or supporting AI or machine learning solutions through professional work, internships, coursework, research, or personal projects.
Foundational Python skills, including the ability to write, read, and debug simple code for data preparation or AI and machine learning tasks; experience may come from work, coursework, or projects.
Familiarity with machine learning, generative AI, or agent-based approaches.
Foundational experience preparing or working with structured or unstructured data for AI use cases, including checking data quality; coursework and project experience are welcome.
Foundational understanding of version control and tooling such as Git.
Exposure to deploying solutions across multistage environments, such as development, test, and production.
Familiarity with AWS, Databricks, or Azure services.
Understanding of foundational data engineering concepts, such as data pipelines and data quality.
Ability to communicate technical concepts clearly within a team and with client stakeholders.

Nice To Haves

Exposure to LLM platforms or tools such as Amazon Bedrock, Azure AI Foundry, or similar.
Experience with agentic tooling such as AWS Quick, Claude, Copilot, OpenClaw, Dots, or Grok Bot.
Exposure to agentic development with tools such as Claude Code, Kiro, or Cursor.
Exposure to MLOps practices, including model versioning or monitoring.
Experience working alongside data engineering teams on shared solutions.
AWS, Azure, or entry-level AI/ML certifications.
Databricks or Snowflake certifications, or willingness to obtain them.
Claude certifications, or willingness to obtain them.

Responsibilities

Complete assigned development tasks for client AI and machine learning solutions, including generative AI and agent-based capabilities, using established approaches and guidance.
Build and test defined components of AI workflows, services, and integrations alongside data and application platforms, following team designs and review processes.
Apply established AI techniques, patterns, and tools to solve well-defined problems.
Run established tests and evaluation procedures, document results and model limitations, and raise unexpected outputs or data-quality concerns for review.
Participate in design discussions, reviews, and team feedback sessions.
Collaborate with data and application engineers on assigned tasks that help prepare AI solutions for production, following established testing, deployment, and review procedures.
Follow project guidance for data usage, bias, transparency, and privacy; document concerns and seek guidance when requirements are unclear.
Write clear, maintainable code for assigned tasks, participate in code reviews, incorporate feedback, and document implementation details and test results.
Continue developing skills in AI, machine learning, and production delivery practices.
Communicate progress, ask questions, and raise blockers within the delivery team; participate in client discussions with support from experienced consultants.