AI Engineer

Requirements

• Experience in designing and building production LLM-powered systems: tool use, orchestration, prompt engineering, error handling.
• Experience working with multiple model providers (OpenAI, Anthropic, Google, etc.) and making informed trade-offs on capability, cost, latency, and task fit.
• Knowledge of Python for AI systems, orchestration, and integrations.
• Knowledge of TypeScript/JavaScript for APIs, integrations, and connecting AI systems to production products.
• Understanding of frameworks or custom implementations for agent workflows, tool calling, and multi-agent coordination.
• Understanding of building and deploying AI systems in cloud environments.
• Hands-on experience designing and building production-grade AI agents using the Microsoft AI ecosystem (Azure AI Foundry, Azure AI Projects SDK, and related services).

Strong plus

• Experience with MCP (Model Context Protocol) or similar integration patterns for tooling and context management.
• Understanding of cloud infrastructure basics for identity, hosting, and deployment fundamentals.
• Familiarity with DevOps tooling, CI/CD, pipelines, and delivery workflows.

Nice to have

• Agents sit at the API/orchestration layer, but reading service code helps when collaborating with the Architect or debugging integrations.
• Experience integrating AI capabilities into existing enterprise products.

Responsibilities

• Build, own, and evolve AI Engage.
• Design and build AI agents, modules, and capabilities from concept to production.
• Develop new interaction patterns, data integrations, and reasoning capabilities.
• Enable deployment both as standalone AI Engage features and as embedded companion agents within AGRIVI products.
• Maintain a clean separation between reusable feature logic and domain-specific context.
• Extend the system to new industry verticals without rebuilding from scratch.
• Own architecture decisions and ensure the system remains scalable and maintainable.
• Deliver production-grade systems that are reliable, observable, testable, and high-quality.
• Drive model and technical strategy.
• Select the right models per use case, balancing capability, cost, latency, and context constraints.
• Run structured evaluations when model decisions have product or cost impact.
• Stay current with the AI landscape and bring relevant insights into product and technical decisions.
• Contribute to product direction.
• Identify where AI creates real value, new features, workflows, or efficiency gains.
• Contribute directly to the AI roadmap and prioritization.
• Work closely with product and engineering to shape what gets built and why.
• Drive internal AI adoption.
• Work alongside the team to identify where AI agents and automation create meaningful leverage in how AGRIVI operates and delivers.
• Improve internal workflows and delivery efficiency by reducing manual work or complexity.
• Take ownership of ideas from architecture through production, and see them through.
• Make technical and product trade-offs independently.
• Build a close working relationship with the CDO, Head of Product, and the engineering team.
• Build real systems that operate in production, not experiments.

We offer

• US and EU projects based on advanced technologies.
• Competitive compensation based on skills and experience.
• Regular performance appraisals to support your growth.
• Flexibility in workspace, either remote or our welcoming office.
• Bonuses for article writing, public talks, other activities
• Generous time off, including vacation, national holidays, sick leaves, and family days.
• Personalized learning programs tailored to your interests and skill development.
• Free tech webinars and meetups organized by Svitla.
• Regular corporate online activities.
• Awesome team and a friendly, supportive community!

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