Don’t just write code. Build what’s next.
At TalentoHC, we’re looking for an AI Solutions Engineer who can turn ideas into working solutions—and use AI to move from problem to production faster. You’ll combine engineering judgment, creativity, and Claude Code to build technology that makes the business better.
Explore. Build. Validate. Improve. Repeat.
MEET THE ROLE: YOUR PURPOSE:
Build practical, AI-enabled solutions that turn complex business needs into working technology—quickly, thoughtfully, and with strong technical discipline.
As an AI Solutions Engineer, you will combine hands-on engineering expertise with advanced use of Claude Code and modern AI development tools to design, build, integrate, test, deploy, and continuously improve applications, automations, internal tools, and AI-powered workflows.
This is a builder role for someone who can move comfortably from an ambiguous business problem to a functioning solution, while maintaining ownership of the technical decisions, quality, security, and long-term maintainability of what they create.
WHERE YOU’LL MAKE YOUR MARK:
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Build Practical Solutions: Translate business needs into working applications, internal tools, APIs, automations, and AI-enabled workflows that create measurable value.
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Engineer with AI as a Multiplier: Use Claude Code extensively across planning, implementation, refactoring, testing, debugging, documentation, and iteration—accelerating delivery without compromising engineering judgment.
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Connect Systems & Data: Design and implement integrations across APIs, databases, SaaS platforms, cloud environments, Microsoft 365, and other business systems.
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Own the Solution End to End: Take ownership beyond code generation, including architecture, validation, testing, deployment, troubleshooting, documentation, monitoring, and ongoing improvement.
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Turn Ambiguity into Execution: Break loosely defined business problems into practical technical approaches, milestones, tradeoffs, and deliverables that can move quickly from concept to implementation.
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Build for Reliability: Review AI-generated code critically and apply sound judgment around security, performance, maintainability, dependencies, technical debt, and failure modes.
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Improve How We Build: Experiment with emerging AI development tools and workflows, identifying where they can meaningfully improve engineering speed, quality, and repeatability.
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Make Technology Understandable: Communicate technical decisions, risks, tradeoffs, and recommendations clearly to business and non-technical stakeholders.
WHAT SUCCESS LOOKS LIKE:
You’ll be successful when you’re:
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Ideas become working solutions: Business challenges move quickly from ambiguity to practical, production-ready technology that delivers real value.
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AI meaningfully accelerates delivery: Claude Code and complementary AI tools increase development velocity while engineering quality, judgment, and control remain uncompromised.
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Solutions are built to last: What reaches production is reliable, secure, tested, documented, and maintainable—not simply a successful prototype.
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Technology eliminates friction: Repetitive work and inefficient processes are transformed into practical, scalable automation.
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Systems work as one: APIs, data, business applications, and AI capabilities connect seamlessly to create smarter, more efficient workflows.
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Every technical decision has intent: Solutions reflect clear engineering judgment, with thoughtful consideration of architecture, tradeoffs, risks, and long-term implications.
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Every build makes the next one better: Reusable patterns, stronger documentation, better guardrails, and AI-assisted practices continuously improve how solutions are delivered.
WHAT MAKES YOU THE ONE:
You bring:
EXPERIENCE & EDUCATION:
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3+ years of relevant experience in software development, systems engineering, automation, DevOps, solutions engineering, technical implementation, or a closely related discipline.
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English proficiency required.
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Demonstrated experience personally building or materially owning applications, integrations, automations, internal tools, or production systems.
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Advanced, hands-on experience using Claude Code on substantial development projects—not simply experimentation or casual AI-assisted coding.
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Experience working across the development lifecycle, including architecture, implementation, testing, debugging, deployment, and maintenance.
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Comfortable translating ambiguous requirements into technical approaches and working deliverables.
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Strong written and verbal communication with the ability to operate effectively in a fast-moving, entrepreneurial environment.
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Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field preferred; demonstrated capability may outweigh formal credentials.
TECHNICAL KNOWLEDGE:
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AI-Assisted Development: Claude Code, GitHub Copilot, Cursor, ChatGPT, Gemini, or comparable development environments and AI tools.
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Software & Application Development: JavaScript/TypeScript, Node.js, React/Next.js, Python, or comparable modern development stacks.
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Systems & Integrations: REST APIs, webhooks, authentication, databases, SQL, SaaS integrations, and Microsoft 365/Microsoft Graph.
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Cloud & Deployment: AWS, Azure, GCP, Vercel, Supabase, DigitalOcean, Vultr, Docker, CI/CD, logging, and monitoring.
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AI & Automation: AI APIs, agentic workflows, multi-agent frameworks, workflow automation, conversational interfaces, or voice AI.
AI DEVELOPMENT MINDSET:
You don't need to use every tool in the ecosystem. You do need to understand how to choose the right one.
You know how to:
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Structure repository context, instructions, reusable prompts, and guardrails.
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Use AI iteratively for planning, coding, testing, debugging, refactoring, and documentation.
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Review AI-generated code rather than blindly accepting it.
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Recognize hallucinations, insecure patterns, dependency risks, silent failures, and overengineering.
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Know when AI should accelerate the work—and when human judgment should slow it down.
COMPETENCIES:
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Technical Judgment: Makes sound decisions about architecture, quality, security, and tradeoffs.
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Builder Mentality: Moves naturally from problem to prototype to functioning solution.
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Learning Agility: Continuously adapts to rapidly evolving AI and technology ecosystems.
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Ownership & Accountability: Remains responsible for the outcome—not simply the code generated.
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Communication & Collaboration: Makes complex technical concepts understandable and works effectively across technical and business environments.
