Digicomplish

You are expected to make and own the right design decisions, set the guardrails, and govern delivery leaning on your specialist engineers to build the deep components.

Required skills

• 10+ years of professional experience, with 4+ years driving Machine Learning / AI projects and solution design.
• Accountable ownership of solution architecture able to set the target architecture, make and defend key design decisions, and be answerable for them to the client.
• Good working understanding of modern GenAI agentic designs and frameworks (e.g., LangChain / LangGraph) and of AI integration patterns, including MCP (Model Context Protocol)-style tool and data integration.
• Ability to define security boundaries and model/tool controls what an AI system is allowed to access and do, guardrails against incorrect or unverifiable outputs, and human-in-the-loop checkpoints.
• Experience establishing delivery governance and release gates clear quality, security, and compliance criteria that each release must pass before go-live.
• Understanding of Cloud services (Azure, Google Cloud Platform, or AWS) and Agile delivery with tools like JIRA and Confluence.
• Excellent communication and stakeholder management to collaborate with senior client SMEs; an entrepreneurial, founder's mindset to own delivery end-to-end.
• Good to have: exposure to financial-services / asset-management valuation workflows or other regulated enterprise domains.

Roles & responsibilities

• Architecture & Technical Accountability:
• Own the solution architecture and the key design decisions across the platform and remain accountable for them with the client.
• Guide integration design, including MCP integration patterns and how the AI system connects to the client's data and tools.
• Learn the client's workflow well enough to translate business needs into a sound, practical technical approach.
• Define security boundaries and model/tool controls access limits, guardrails, and human-review checkpoints that keep sensitive outputs correct and controlled.
• Provide technical direction to the engineering team on solutioning and system design; align them to a technical roadmap and ensure timely execution.
• Delivery Governance & Release Gates:
• Establish and enforce release gates the quality, security, and compliance signoffs required before each release.
• Own overall delivery so scope, quality, and timelines are consistently met; manage the big-picture program timeline (releases, phases, go-live plans) using engineering velocity/capacity inputs from the EM.
• Ensure delivery decisions reflect cost, ROI, and long-term business impact.
• Identify delivery risks, create proactive mitigation plans, and track program health across all milestones.
• Ensure robust business-facing documentation requirements/BRDs/PRDs, implementation plans, and roadmaps.
• Client Relationship & Communication:
• Lead discussions with the client to shape the AI roadmap and expand into new processes.
• Act as the primary point of contact for communication, feedback, and escalations; manage expectations proactively.
• Evaluate use-cases for new development and unlock new value for the client.
• Participate in the client's internal stakeholder meetings to capture, clarify, and consolidate requirements into actionable product needs.
• Team Leadership & Coordination:
• Drive cross-functional alignment across engineering, product, and client teams.
• Remove blockers for client and internal teams through clear communication and effective prioritization.
• Conduct regular 1:1s focused on support, delivery alignment, and well-being.
• Acknowledge new client requests promptly and partner with the EM to assess feasibility, capacity, and timeline impact before committing.