AI Engineer

QArea is looking for an AI Lead who will take ownership of the AI direction across client projects and help us build strong AI expertise within our development teams.

This is a hands-on leadership role at the intersection of AI architecture, engineering, pre-sales, and technical mentoring. You will work with client requests, transform AI ideas into realistic technical solutions, support projects from discovery and estimation to production, and help development teams adopt effective AI practices.

We are looking for someone who not only understands modern LLM technologies but has real production experience and can make pragmatic decisions based on business value, cost, quality, latency, security, and scalability.

What you’ll do

• Design and drive AI features for client products — from initial idea and pre-sales to production.
• Turn high-level client requests into technically sound, realistic, and properly estimated AI solutions.
• Own the technical quality of the AI part of projects.
• Participate in client communication, discovery sessions, technical pre-sales, and project estimation.
• Prepare technical proposals, architecture diagrams, timelines, and cost estimates, including inference costs (tokens, GPU, hosting).
• Build PoCs and demos within short discovery cycles (typically 1-2 weeks).
• Select appropriate models, providers, and infrastructure based on quality, cost, latency, security, and data privacy requirements.
• Create reusable reference architectures, templates, and starter kits for AI solutions, including RAG-based systems.
• Build and share AI expertise across development teams.
• Mentor engineers and help teams evaluate and adopt new AI technologies and approaches.

What we’re looking for

• 7+ years of commercial software development experience.
• 3+ years of experience as an Architect, Tech Lead, or Senior Engineer.
• 2+ years of hands-on experience with LLMs in production.
• At least 3 original AI/LLM projects launched into production that you can discuss in detail — including challenges, failures, and how you solved them.
• Experience in team management and technical mentoring.
• Experience with pre-sales, project estimation, and direct client communication.
• English — Upper-Intermediate or higher (B2-C1), sufficient to independently lead client calls.

Technical expertise

We expect solid practical experience with:

• LLM APIs: OpenAI, Anthropic, Google Gemini.
• Cloud AI platforms: AWS Bedrock, Azure OpenAI, Vertex AI.
• RAG: chunking, embeddings, hybrid search, reranking, vector databases (pgvector, Qdrant, Pinecone, Elasticsearch/OpenSearch).
• AI agents & tool use: function calling, MCP, orchestration frameworks such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK, Semantic Kernel, or similar.
• Understanding the limitations and risks of autonomous agents.
• Prompt & context engineering: structured outputs, prompt caching.
• Evals & LLMOps: test datasets, LLM-as-judge, tracing and monitoring tools such as Langfuse, LangSmith, promptfoo, or similar.
• Open-source/self-hosted models: vLLM, Ollama, llama.cpp.
• Basic understanding of quantization and GPU requirements.
• Python and/or TypeScript at a level sufficient to independently build a PoC.
• AI security: prompt injection, data leakage, OWASP Top 10 for LLM, PII.
• AI unit economics: ability to estimate cost per request/month and identify optimization opportunities.

Nice to have

• Experience with Semantic Kernel and Microsoft.Extensions.AI.
• Fine-tuning experience (LoRA, etc.) and a practical understanding of when fine-tuning is actually justified.
• Experience with on-device AI: Core ML, Apple Foundation Models, Gemini Nano, TensorFlow Lite / MediaPipe.
• Multimodal AI: vision, speech-to-text, TTS, image generation.
• Classical ML: classification, recommender systems, analytics.
• Experience integrating AI into CMS and e-commerce platforms such as WordPress, Shopify, or headless CMS.

About the role

This role is a good fit for someone who wants to combine hands-on AI engineering with technical leadership and business impact.

You’ll have an opportunity to influence how AI solutions are designed and delivered across different client projects, establish reusable engineering practices, and help development teams strengthen their AI expertise.

We value a pragmatic approach to AI: choosing technologies because they solve the problem effectively — not simply because they are new.

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