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.

Prototype rapidly, then harden to production

build POCs for agentic workflows (multi-step tasks, tool-use, orchestration), validate with users, and evolve to production-ready implementations with testing, monitoring, and secure deployment patterns.

Own end-to-end delivery from architecture through implementation

define build/test specifications, create eval and quality gates for GenAI outputs, and produce operational guides/runbooks for reliability, safety, and maintainability. Identify and mitigate business/technical risks specific to AI systems (data access, prompt injection, hallucinations, privacy, compliance, latency, cost); propose safeguards and fallback behaviors for agentic flows.

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. Balance platform configuration with extensibility to maximize speed and maintainability. Co-engineer with customer teams to ensure knowledge transfer, documentation, and reusable patterns. Drive delivery plans across the full lifecycle—discovery, build, eval, deployment, and operations—accounting for dependencies across data, security, and enterprise systems.

Build agentic workflows that invoke tools/APIs safely (function calling patterns), with guardrails, validation, and auditability. Implement evaluation strategies (offline/online) to measure output quality, groundedness, safety, and business impact. Capture repeatable assets (templates, reference architectures, accelerators) and share feedback loops with internal teams. Experienced migration talent especially welcome. Dynamics 365 Customer Engagement — Salesforce takeouts (SF Takeout), Contact Center as a Service (CCaaS), and standard CE (Sales / Customer Service / Field Service).

Azure Fabric & Azure AI Foundry — One Lake-based unified data management, Lakehouse architecture for analytics and AI, model catalog usage for model discovery and fine-tuning, enterprise agent development and multi-agent orchestration via Foundry Agent Service, data grounding and RAG using One Lake and Azure AI Search. 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).