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Responsibilities
AI strategy and assessment
- Conduct AI readiness assessments and data maturity evaluations that help clients understand their current capabilities and see where AI and automation can support value creation.
- Translate business challenges into prioritized Data and AI use cases and help shape transformation roadmaps that align technology investments with client objectives.
- Research emerging AI capabilities (large language models, machine learning, data management, generative AI, and agentic AI) and explain them in clear, accessible terms for non-technical stakeholders.
Solution design and delivery
- Design and prototype agentic AI solutions across our core solution types: document processing and workflow automation; automated communication and prediction analytics; system integration and data synchronization; real-time external data validation; and business intelligence with work prioritization.
- Build proof-of-concept solutions and demos that help business teams explore the potential of Data and AI and analytics approaches, applying a structured approach to comparing models and options so recommendations are well-reasoned.
- Support SOW-based engagement delivery across discovery, kickoff, weekly status, and final delivery, contributing alongside the delivery team.
Responsible AI and governance
- Help clients adopt AI responsibly, sustainably, and compliantly, building appropriate safeguards and monitoring into solution design.
- Contribute to AI and data governance frameworks that promote responsible, sustainable, and compliant use of AI.
Client partnership
- Act as a bridge between technical and business domains, the role our team calls a Data Translator / Business Engineer, turning technical detail into business value.
- Partner with business stakeholders to understand their needs and help translate them into data- and AI-driven solutions.
- Present concepts and findings to business audiences in clear language, including through workshops and working sessions.
Required Qualifications
- Bachelor’s or Master’s degree in Computer science, Data Science, Engineering, or a related quantitative field.
- 3+ years of applied AI, data management, or machine learning experience. Strong internship, research, or academic project work is welcome in place of full-time experience.
- Hands-on experience building AI or machine learning solutions, including work with large language models, natural language processing (NLP), or agentic AI approaches.
- Strong programming skills in Python, with experience using modern AI development tools.
- Ability to move between technical detail and business value, and to communicate clearly with non-technical stakeholders.
- Strong analytical and problem-solving skills, with a structured, well-tested approach to AI work.
Preferred Qualifications
- Experience with agentic or autonomous agent design and AI-assisted development workflows.
- Exposure to taking AI or machine learning solutions beyond a notebook toward deployment, for example through a major cloud platform, containerization, or basic monitoring, gained via internships, research, or coursework.
- Exposure to responsible AI practices such as model evaluation, explainability, and fairness or drift checks.
- Familiarity with business intelligence tools (for example, Power BI or Tableau) and modern data platforms (for example, Snowflake, Databricks, or Spark).
- Exposure to client business systems and ERPs (NetSuite, QBO/IES, Acumatica, or Sage) is a plus.
- Interest in client-facing advisory work and a helpful, customer-oriented approach.
