AI​/ML Engineer - Consultant​/Senior Consultant​/Principal

Position: AI/ML Engineer - Consultant/Senior Consultant/ Principal - (US-CENTRAL)
Who You’ll Work With
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* Please note:

 This role is not eligible for 100% remote work. Employees must live within a commutable distance of a Slalom office and must be willing to be onsite at the client and/or Slalom office when needed. This role could require travel to support the region between 10-20%.
** As a modern technology company, Slalom technologists are disrupting the market and bringing to life the art of the possible for our clients. We have a passion for building strategies, solutions, and creative products that help clients solve their most complex and interesting business problems.

Slalom’s AI Systems & Platforms team focuses on next-level AI, machine learning, and production AI systems for enterprise clients. You’ll join a diverse team of engineers, data scientists, architects, Responsible AI specialists, and AI thought leaders who work across modern cloud, data, and AI platforms. We build practical, scalable AI solutions, partner with leading technology platforms, and help clients move from AI ambition to production outcomes.

What You’ll DoAs an AI Engineer, you will be a hands-on builder responsible for designing, building, evaluating, and deploying production-grade AI systems for clients. You will contribute to solution architecture, own implementation work streams, write production-quality code, and clearly articulate technical decisions, delivery progress, risks, and business value to client stakeholders.

You will:
* Design and build AI-enabled applications and systems, including LLM-powered applications, retrieval-augmented generation solutions, agentic workflows, APIs, data pipelines, and cloud-native services.
* Use modern AI platforms, hyperscaler services, data platforms, and software engineering practices to deliver reliable, secure, maintainable solutions.
* Contribute to AI solution architecture, including application design, cloud service selection, data integration patterns, model/service integration, evaluation strategy, deployment approach, and production operating considerations.
* Own implementation work streams from design through delivery, including backlog refinement, technical design, coding, testing, integration, deployment, documentation, and client handoff.
* Work hands-on with at least one major hyperscaler such as AWS, Azure, or GCP, and at least one major enterprise data platform such as Databricks or Snowflake.
* Partner with clients to understand business processes, technical environments, data constraints, and user needs, then translate those inputs into deployable AI solutions.
* Build and integrate AI systems with enterprise data sources, applications, APIs, workflow tools, and existing client technology ecosystems.
* Apply practical data science and machine learning methods where appropriate, including classification,clustering, segmentation, recommendations, NLP, semantic search, experimentation, and model evaluation.
* Develop evaluation approaches for AI systems, including accuracy, groundedness, relevance, reliability, latency, cost, usability, and business impact.
* Collaborate with Responsible AI specialists to apply responsible AI practices, including appropriate human oversight, risk awareness, validation, documentation, and safe use of AI development tools.
* Responsibly use AI coding assistants and agentic development tools such as Claude Code, OpenAI Codex,Antigravity,Cursor, Git Hub Copilot or comparable tools to accelerate delivery while maintaining code quality, security, testing, and human review.
* Create reusable assets such as reference architectures, accelerators, code templates, demos, implementation patterns, and enablement materials.
* Communicate technical concepts, architecture decisions, implementation tradeoffs, delivery…