Senior AI/ML Consultant

About the role

This role is not eligible for 100% remote work. Employees must live within a commutable distance of our Detroit 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%.

Responsibilities

• 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.
• 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.
• Partner with clients to understand business processes, technical environments, data constraints, and user needs, then translate those inputs into deployable AI solutions.
• 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.
• Responsible use of 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.

Requirements

• 4+ 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 in Python (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 hyperscalersuch as AWS, Azure, or GCPinfrastructure 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.