Consultant

Job description

Embed with customer stakeholders to understand business workflows, constraints, and success metrics; translate ambiguous needs into clear requirements, solution hypotheses, and sprint deliverables for GenAI/agentic scenarios.

Support deployment and live stabilization

troubleshoot integrations and model behavior in real environments, iterate quickly, and ensure successful go-live with clear ownership, monitoring, and incident playbooks. Experienced migration talent especially welcome. Bachelor's degree in computer science, Engineering, or related field AND demonstrated experience delivering production software solutions (or equivalent experience). Proven experience delivering hands‑on, production-grade solutions across the full delivery lifecycle, including requirement ambiguity, development, deployment, and post‑go‑live stabilization.

Proven delivery of Dynamics 365 CE/Power Platform solutions, M365 Copilot Solutions, plus experience building AI-infused workflows (recommendations, summarization, case routing, contact center assist, sales/service copilots) with production-grade engineering practices. Experience building or extending solutions involving Copilots, AI‑assisted workflows, or intelligent automation is a strong plus. Certifications and/or experience in Azure Solutions, Dynamics 365, M365 Copilot and Power Platform; exposure to Azure AI services and GenAI solution patterns is a strong plus.

Agile delivery, DevOps, CI/CD, and operational readiness (observability, incident response) strongly preferred—especially for AI systems. Desirable experience includes Azure solution development, Contact Center solutions, Copilot Studio, and building GenAI/agentic experiences (tool-use orchestration, retrieval grounding, evaluation/quality loops, safety/guardrails).

Excellent communication

explain AI behaviors, risks, and tradeoffs to technical and non-technical stakeholders; drive alignment from ambiguity to execution.

Trusted customer-facing engineering presence

collaborate deeply, pair-build with customer teams, and transfer knowledge to ensure long-term maintainability. Proactively identify and mitigate risks, including AI-specific failure modes (grounding gaps, unsafe outputs, data leakage) and enterprise constraints (security, privacy, compliance).

IC role

influence without authority through strong engineering judgment, structured problem-solving, and crisp stakeholder communication. Delivers working GenAI/agentic solutions from prototype to production with measurable customer impact. Implements guardrails and evaluation to ensure reliability, groundedness, safety, and cost/performance targets. Leaves behind reusable assets, documentation, and enabled customer teams (no dependency).