Generative AI Engineer

Broad Scope & Purpose:

The AI Engineer role is focused on bottom-up AI innovation and automation across the organization. The core purpose of the role is to identify everyday friction in workflows and solve it through small, practical, production-ready AI solutions that can be rapidly built, tested, and adopted by business users.

The role operates at the intersection of technology, business productivity, and change enablement. Rather than building large platforms, the AI Engineer focuses on high-impact micro-solutions—using LLMs, APIs, automation tools, and custom GPTs—to simplify work across documents, emails, data handling, approvals, and repeatable decision flows. Speed to value, usability, and adoption are critical success markers.

The role acts as a force multiplier, enabling leaders and teams to leverage AI safely and effectively in their day-to-day work. A key part of the mandate is not just delivery, but systemization—turning successful use cases into reusable templates, GPTs, and playbooks that can scale organically across teams.

Key Responsibilities:

Problem Identification & Scoping Partner with business, leadership, and functional teams to identify repetitive, high-friction workflows suitable for AI or automation. Translate problem statements into clear AI-ready specifications, including scope, inputs, outputs, success criteria, and acceptance checks. Define the smallest viable solution that delivers meaningful value, avoiding over-engineering.

AI Solution Design & Development Design, build, and deploy lightweight AI solutions using LLMs, APIs, webhooks, and automation platforms. Develop and manage Custom GPTs with strong system instructions, structured prompts, tool connections, and curated knowledge sources. Integrate AI workflows across common enterprise tools such as documents, email, chat, forms, and spreadsheets.

Rapid Prototyping & Iteration Ship proof-of-concepts quickly, test in real business environments, and iterate based on user feedback and observed usage. Validate solutions using edge-case testing, small evaluation sets, and human review, especially for high-impact outputs. Continuously refine prompts, workflows, and automations to improve accuracy, reliability, and usability.

Systemization & Reusability Convert successful use cases into reusable GPTs, templates, automation flows, and playbooks. Standardize repeatable patterns to enable self-service adoption by teams without ongoing technical support. Maintain libraries of prompts, workflows, and solution templates for easy discovery and reuse.

AI Adoption & Enablement Drive bottom-up AI adoption through demos, show-and-tells, walkthroughs, and simple documentation. Enable business users to confidently leverage AI tools with minimal training and clear usage guidance. Act as an internal AI evangelist, showcasing practical use cases and measurable productivity gains. Delivery Management & Stakeholder Coordination Maintain a visible and prioritized AI delivery pipeline covering ideas, builds, pilots, and stabilized solutions. Collaborate closely with leadership offices and cross-functional teams to align priorities and manage expectations. Escalate risks, trade-offs, and decision points proactively to ensure timely and effective delivery.

Ideal Candidate Profile:

Experience 3–4 years of hands-on experience in building production-grade software, AI workflows, or intelligent automations. Proven experience in designing and deploying LLM-based solutions, including prompt engineering, workflow orchestration, and API integrations. Demonstrated ability to rapidly prototype, test, debug, and iterate solutions in real business environments. Prior exposure to enterprise or business-user-facing tools, where usability, reliability, and adoption are critical.

Academic Qualifications: Bachelors/Masters in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical discipline. Strong applied skills and hands-on delivery experience will be valued over purely academic or research-focused credentials.

Desired Competencies:

Functional Competencies Strong capability to design, build, and deploy LLM-based solutions and AI automations, including prompt engineering, workflow orchestration, and API integrations. Ability to translate business problems into clear AI specifications, defining inputs, outputs, success criteria, and verification checks. Hands-on expertise in rapid prototyping and iteration, with a focus on usability, reliability, and real-world adoption by business users. Competence in systemizing AI solutions into reusable GPTs, templates, and automation playbooks that enable self-service usage. Awareness of AI governance, data privacy, and security considerations in an enterprise environment.

Behavioral Competencies Execution-first mindset with a bias toward action, experimentation, and learning through real usage. Strong ownership and accountability, taking end-to-end responsibility from problem definition to adoption. Clear, structured communicator who can simplify AI concepts for non-technical stakeholders and build confidence in AI usage.