Job description

Who You’ll Work With **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 Do As 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 workstreams, 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 workstreams 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, GitHub 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 progress, risks, and outcomes to client stakeholders ranging from engineers to executives. • Mentor peers and clients on AI engineering practices, production delivery patterns, responsible AI basics, and practical use of modern AI development tools. • Stay current on emerging AI engineering patterns, hyperscaler capabilities, data platform features, LLM application architectures, agentic workflows, and production AI practices. What You’ll Bring • 3+ years of experience designing and building data, AI, software, or machine learning solutions in real-world business environments. • Hands-on experience building production-quality software using Python and modern software engineering practices. • Hands-on experience with statistical and data mining software packages inPython (e.g. SciPy, NumPy, Pandas, SciKit-Learn, glmnet, caret, dplyr) and a knowledge of a variety of statistical modeling and machine learning algorithms as well as their practical application. • Experience contributing to technical architecture and owning implementation workstreams in client-facing or cross-functional delivery environments. • Experience delivering AI or ML solutions beyond notebook prototypes, including deployment, integration, testing, monitoring, and operational support. • Experience with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Google Vertex AI, Databricks Mosaic AI, Snowflake Cortex, or comparable enterprise AI platforms. • Experience with vector databases; embeddings models; search platforms; AI Agentbuildand orchestration frameworks; LLM evaluation tools; and AI observability tooling. • Experience with APIs, SQL, data pipelines, ETL/ELT, cloud services, and enterprise data integration patterns. • Experience with LLM application patterns such as RAG, embeddings, semantic search, prompt orchestration, tool/function calling, or agentic workflows. • Practical understanding of model and AI-system evaluation, including validation methods, test datasets, quality metrics, error analysis, and production feedback loops. • Working knowledge of responsible AI basics, including privacy, security, bias, explainability, human review, safe tool use, and appropriate governance escalation. • Strong consultative and communication skills, including the ability to explain complex technical concepts to business and technical stakeholders. • Ability to work independently, collaborate across multidisciplinary teams, and deliver high-quality client outcomes in ambiguous environments. • Curiosity and continuous learning mindset across AI platforms, software engineering, cloud, data, and applied machine learning. Preferred Qualifications • Technical depth in at least one major data platform such as Databricks or Snowflake. • Technical depth in at least one major hyperscaler such as AWS, Azure, or GCP infrastructure and data services in addition to AI services. • Experience using AI-assisted development tools responsibly to improve delivery productivity while maintaining engineering discipline. • Experience building agentic workflows or AI-enabled business process automation. • Experience with MLOps, LLMOps, CI/CD, infrastructure-as-code, automated testing, monitoring, logging, cost management, or production support. • Experience in consulting, client delivery, technical pre-sales, solution shaping, or cross-functional product engineering environments. • Exposure to industry-specific AI use cases in areas such as financial services, healthcare, life sciences, manufacturing, retail, public sector, or technology. • Experience mentoring engineers, data scientists, or client teams on modern AI engineering practices. About Us Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams collaborate with clients to bring powerful customer experiences, innovative ways of working, and new products and services to life. We are trusted by leaders across enterprise, mid-market, and public sector organizations to improve operations, drive growth, and create value. Compensation and Benefits Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance.   Slalom is committed to fair and equitable compensation practices. For this position, the targeted base salary range at the Consultant level is $106,000 - $133,000 and at the Senior Consultant level is $133,000 – $166,000. In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time. We are committed to pay transparency and compliance with applicable laws. If you have questions or concerns about the pay range or other compensation information in this posting, please contact us at:  peopleone@slalom.com . Please note, this recipient is not able to support recruitment inquiries beyond this purpose. EEO and Accommodations Slalom is an equal opportunity employer and is committed to attracting, developing and retaining highly qualified talent who empower our innovative teams through unique perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements. Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team or contact  accomodationrequest@slalom.com  if you require accommodations during the interview process. Locations: Austin, TX; Chicago, IL; Columbus, OH; Dallas, TX; Detroit, MI; Houston, TX; Kansas City, MO; Minneapolis, MN; Nashville, TN; St. Louis, MO

Requirements

Must-haves

Who You’ll Work With
What You’ll Do
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.
Own implementation workstreams 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.
Create reusable assets such as reference architectures, accelerators, code templates, demos, implementation patterns, and enablement materials.
Communicate technical concepts, architecture decisions, implementation tradeoffs, delivery progress, risks, and outcomes to client stakeholders ranging from engineers to executives.
Mentor peers and clients on AI engineering practices, production delivery patterns, responsible AI basics, and practical use of modern AI development tools.
Stay current on emerging AI engineering patterns, hyperscaler capabilities, data platform features, LLM application architectures, agentic workflows, and production AI practices.
What You’ll Bring

Nice-to-haves

Technical depth in at least one major data platform such as Databricks or Snowflake.
Technical depth in at least one major hyperscaler such as AWS, Azure, or GCP infrastructure and data services in addition to AI services.
Experience using AI-assisted development tools responsibly to improve delivery productivity while maintaining engineering discipline.
Experience building agentic workflows or AI-enabled business process automation.
Experience with MLOps, LLMOps, CI/CD, infrastructure-as-code, automated testing, monitoring, logging, cost management, or production support.
Experience in consulting, client delivery, technical pre-sales, solution shaping, or cross-functional product engineering environments.
Exposure to industry-specific AI use cases in areas such as financial services, healthcare, life sciences, manufacturing, retail, public sector, or technology.
Experience mentoring engineers, data scientists, or client teams on modern AI engineering practices.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningllmawsazuregcppythonmlsqlci/cdagile

Platform: slalom
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave