AI​/ML Engineer – Consultant or Senior Consultant

AI/ML Engineer – Consultant or Senior Consultant

Candidates must reside in the Twin Cities, MN area and be comfortable commuting as needed. This is a hybrid role with onsite expectations.

As a modern technology company, our Slalom technologists are disrupting the market and bringing to life the art of the possible for our clients. We have passion for building strategies, solutions, and creative products to help our clients solve their most complex and interesting business problems. We surround our technologists with interesting challenges, innovative minds, and emerging technologies. Slalom's Data AI capability focuses on next-level AI and Machine Learning solutions for clients.

You'll join a diverse team of engineers, data scientists, and AI thought leaders. We work across modern AI platforms, develop novel AI solutions, and work with our technology partners to push their products into the future.

• Apply Machine Learning, Deep Learning, and other relevant technologies to assist in building AI tools that solve real business problems.
• Assist in the design, development, presentation, and delivery of traditional ML solutions in areas such as Computer Vision, NLP, Recommendation Systems, etc.
• Stay updated on fundamental AI/ML concepts and emerging trends to contribute to solution discussions and development.
• Contribute to the growth of the AI/ML community within the organization through learning, mentoring, and sharing knowledge.
• Design and deliver LLM-enabled applications (RAG, tool/function calling, structured outputs) that solve client problems in finance and operations.
• Build document intelligence pipelines for financial documents (invoices, statements, contracts, tickets), including ingestion, OCR/layout extraction where needed, entity extraction, normalization, and quality checks.
• Develop and deploy anomaly detection solutions for financial datasets (transactions, journals, reconciliations, forecasts), using statistical, ML, and/or deep learning approaches; define thresholds, alerting, and feedback loops.
• Implement agentic workflows that safely orchestrate multi-step tasks (e.g., triage → retrieve → reason → propose action → human approval → execute), with auditability and clear handoffs.
• Integrate AI solutions into enterprise ecosystems (APIs, data platforms, ERPs/work management tools), partnering with client security, data, and architecture teams.
• Establish LLMOps/MLOps practices: evaluation harnesses, prompt/model versioning, test automation, monitoring/observability, cost/performance tuning, and rollback strategies.

• 3-6 years of experience implementing models or machine learning algorithms into production.
• 1-3 years of professional Managed or IT Professional consulting experience.
• Strong consultative and communication skills, and the ability to convey complex technical concepts to diverse stakeholders.
• Solid technical foundation, combined with constant curiosity to stay on the creative edge of ML/AI solutions.
• Technical and conversational fluency in AI, GenAI, Machine Learning and at least one of the major clouds (AWS, GCP, Azure).
• Hands‑on experience with RAG, agentic workflows, fine tuning, or other LLM‑based techniques.
• Experience deploying ML/AI into production, and ability to talk through best practices and pitfalls.
• Understanding of validation frameworks for AI and ML tools, with knowledge of the production‑ready offerings on the market for AI validation.
• Strong Python for ML/AI (e.g., data wrangling, model development, APIs, testing); solid SQL and data modeling fundamentals.
• Hands‑on GenAI experience: RAG, prompt engineering, embeddings/vector search, function/tool calling, agentic orchestration, and/or fine‑tuning.
• Experience with information extraction / entity extraction (e.g., NER, structured extraction from unstructured text, classification, normalization, confidence scoring).
• Experience designing ML/AI systems with production considerations: reliability, latency, cost, observability, privacy/security, and safe failure modes.

• Experience in a sales support role, within Data Science, Machine Learning, or AI.
• Familiarity with leading…