AI Practitioner

Job overview We are looking for a Senior AI Practitioner with a strong software engineering background who can assess development workflows, identify where AI tools add real value, and deliver practical, high-impact recommendations. The right person comes to a project, performs a structured assessment, prioritizes opportunities — some requiring training, others involving configuration, access, or security changes — presents an action plan to the client, and executes on it. The role requires hands-on experience with AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Amazon Q) and the ability to design solutions that fit the team's context, not upsell. Stakeholder communication, workshop delivery, and documentation are part of the job, but the core value is in assessment and recommendations grounded in real engineering experience. Responsibilities • Assess development workflows and identify high-value AI integration opportunities • Design AI solutions by selecting appropriate tools, techniques, and prompting approaches • Recommend appropriate AI tools based on team needs, project context, and practical fit • Present assessment findings and action plans to stakeholders; manage expectations • Drive team AI adoption through pair-working, demos, and hands-on enablement • Configure and use GenAI tools hands-on across delivery workflows • Build lightweight automations and integrations, including API-based scenarios • Deliver training and knowledge transfer tailored to audience and AI maturity level • Apply responsible AI practices — mitigate risks such as data leakage, hallucinations, and bias • Document and share AI practices to raise organizational capability Requirements Must have: • Strong software engineering background (6+ years) with understanding of the full SDLC — not limited to a single phase • Experience in at least one major stack (e.g., .NET, Java, Python, JavaScript/TypeScript) • Practical understanding of how LLMs work and when to apply different approaches (RAG, prompt engineering, fine-tuning) — focus on informed decision-making, not deep theory • Strong prompt engineering: systematic prompting, structured outputs, scenario adaptation • Deep hands-on experience with at least one AI-assisted development tool (e.g., GitHub Copilot, Claude Code, Amazon Q, OpenAI Codex) • Ability to work independently and deliver reliably (Competent level per framework) • Experience in workflow analysis and AI solution design • Strong communication across audiences: recommendations, sessions, training • Experience delivering live demos, workshops, or pair-working sessions to drive adoption • Experience creating playbooks, guidelines, reports, or reusable learning materials • Awareness of responsible AI concerns: privacy, hallucinations, safe usage • Broad delivery understanding to spot AI opportunities across adjacent roles Nice to have: • Experience building AI-enabled automations, internal helpers, or lightweight end-to-end workflows • Experience integrating AI via APIs or configuring custom assistants / GPT-style workflows • Familiarity with multiple AI product categories (coding assistants, conversational AI, productivity copilots) • Ability to evaluate practical applicability of new AI tools across the broader ecosystem • Contributions to communities of practice, knowledge bases, or mentoring of less experienced practitioners • Experience defining and tracking AI impact metrics: time saved, quality improvements, adoption rates, ROI • Cross-industry exposure or ability to adapt recommendations to different client contexts