Design, build, and deploy practical AI-powered solutions for internal Eskwelabs use. Fellows in this track work on AI agents, AI workflows, and robotic process automation (RPA) systems that combine deterministic logic with non-deterministic AI components (e.g. LLMs). The focus is on turning real operational or product needs into working AI solutions—using clean logic, thoughtful prompts, and lightweight architecture. AI tools are used both as the subject of the work and as a productivity multiplier during development.

Your work will contribute to the following outcomes:
‍1. Working internal AI agents and workflows that automate, augment, or accelerate Eskwelabs operations (e.g. analytics, content creation, ops, QA, research).
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2. Well-structured AI solutions that balance deterministic systems (rules, scripts, pipelines) with non-deterministic AI behavior (LLMs, embeddings, classifiers).
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3. Reusable prompts, components, and workflows that can be extended by future fellows, developers, or AI agents.
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4. Improved AI product thinking across Eskwelabs—clear use cases, better prompt design, realistic expectations of AI strengths and limits.

A. AI Agents
Build AI agents that can execute defined tasks with minimal human intervention. Fellows design the agent’s role, inputs, tools, memory (if any), and outputs, and implement it using Python-based or low-code frameworks.

‍Example: Create an internal “Learning Success Coach” agent that draws a learner’s project output, pulls relevant learning sources, runs basic analysis, and generates a personalized learning success assistance plan to share to their mentor and instructor.
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‍B. AI Workflows & Robotic Process Automation
Design multi-step workflows that combine deterministic steps (scripts, rules, APIs) with AI-powered steps (classification, summarization, generation). Emphasis is placed on reliability, fallbacks, and clear logic.

‍Example: Build a workflow that ingests learner feedback, classifies sentiment and themes, summarizes insights, and pushes a weekly action email to the learning program team.

This is a task-based, production-oriented AI development role. Work arrives as concrete internal use cases rather than abstract research problems. Fellows are expected to think logically, write clearly, and translate needs into functioning AI systems. The role blends junior software development, prompt engineering, and lightweight product management. AI tools are heavily used for coding, ideation, debugging, and iteration—but success depends on human judgment, testing discipline, and clear communication.

This track is ideal for individuals who want hands-on experience building real AI systems and are curious about how AI actually works in production—not just in demos.

You might come from backgrounds such as:
- Computer Science & Engineering: students or early-career developers exploring AI systems.
- Operations Management, Data & Analytics: low-code analysts interested in moving closer to automation and AI tooling.
- Product, Ops, or Tech-Curious Roles: individuals who enjoy designing systems and workflows, even without deep prior engineering experience.

What matters most is not expertise, but mindset. You should enjoy learning fast, thinking logically, and experimenting with how AI can solve real problems.

- Building AI agents and multi-step AI workflows
- Prompt engineering for reliability, clarity, and use-case fit
- Basic AI architecture and system design
- Python-based AI development (notebooks, scripts, APIs)
- Product thinking: defining use cases, success criteria, and constraints
- Testing and evaluating non-deterministic AI systems
- Using AI to accelerate development work itself

Traits
- Eagerness to learn and experiment with new AI tools
- Logical thinking and comfort with step-by-step reasoning
- Strong written communication for prompts, specs, and documentation
- Creativity in imagining use cases and edge cases
- Willingness to test, break, and refine AI behavior

‍Skills
- Basic Python programming or scripting
- Understanding of APIs and simple workflows
- Prompt engineering and iterative testing
- AI-assisted coding and debugging
- Clear documentation and explanation of system behavior

- AI Engineer / LLM Engineer: building and maintaining AI systems
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- Software Developer (AI-Focused):  integrating AI into products and tools
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- AI Product Manager: defining use cases and guiding AI feature development
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- Automation / RPA Specialist: scaling AI-enabled operations

- AI Solutions Architect (Junior → Senior): designing end-to-end AI systems

Tasks are listed in a shared tracker with a problem description, expected behavior, deadline, and submission format. Fellows claim tasks, build solutions independently or in small groups, and submit code, prompts, and documentation.

Fellows are expected to ask clarifying questions early, especially around use cases and constraints. Submissions typically include code or workflow artifacts, prompt versions, and a short explanation of design choices. Tasks are reviewed and marked Approved or Revisions Needed.

You will report to the AI Solution Development team. They meet with fellows twice a month to review solutions, discuss design trade-offs, and demonstrate better patterns for agent design, prompt evaluation, and AI architecture.

You will be judged on whether your AI solutions work, are logical, and are usable by others. Strong performance includes thoughtful prompt design, clear system structure, good test coverage, and honest documentation of limitations—not just flashy outputs.