2–6 years of experience in technology projects (application development, implementation, systems integration, or client-facing delivery)
Degree in Computer Engineering, Electrical Engineering, Telecommunications, Data Science, or similar field (or equivalent practical experience)
Experience or exposure to AI-enabled platforms, digital solution implementation, or automation projects
Solid understanding of APIs and system integration, including REST, common integration patterns, and enterprise workflows
Practical programming skills in Python and/or JavaScript/TypeScript
Ability to understand business requirements and translate them into technical solutions
Strong analytical, problem-solving, and communication skills; comfortable collaborating with cross-functional and multicultural teams
Interest in and/or experience with Generative AI, including LLMs, and AI-enabled components (automation, agents)
Understand the product’s capabilities, limitations, configuration options, and extension points
Analyze functional and technical requirements with customer and delivery teams and translate them into implementable solutions
Configure, adapt, and implement AI-enabled solutions aligned to specific customer use cases
Design and support integrations between the platform and enterprise systems using APIs, authentication/authorization, and secure integration practices
Participate in technical solution design to ensure alignment with customer architecture and operational constraints
Implement and support flows, automations, agentic components, tool calling/function calling, and other AI-enabled platform features
Test, validate, debug, and optimize solutions to meet quality, reliability, and performance requirements
Apply AI knowledge (where applicable) including LLM, Speech-to-Text (STT), and Text-to-Speech (TTS) capabilities to support customer needs
Contribute to reusable assets, implementation patterns, best practices, and technical documentation
Identify opportunities to improve platform usage and consistency across multiple geographies/markets
For more senior profiles (typically 3–6 years): lead or co-lead use case analysis, assess trade-offs (latency/quality/cost/scalability), identify risks and mitigations, and support junior engineers in problem-solving
Familiarity with different market LLMs/providers, their strengths/limitations, and evaluation approaches
Knowledge of prompting techniques, prompt testing, and prompt evaluation methods
Exposure to agentic frameworks, multi-agent patterns, MCP/tool-use concepts, or orchestration approaches
Experience with SaaS platforms, cloud environments, or enterprise architectures
Exposure to observability/logging/debugging tools and troubleshooting in production-like environments
Experience working on client-facing initiatives or in international/multi-geography contexts
Familiarity with STT/TTS considerations (quality, latency, language support, multilingual requirements)
