Senior AI Engineer

⋮IWConnect

Senior AI Engineer leading AI projects and developing enterprise-grade AI applications in a hybrid environment. Involved in the entire AI solution lifecycle from architecture to deployment.

Posted 7/5/2026full-time🇲🇰 North MacedoniaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying AI solutions, with a strong focus on production-grade code and delivery quality. Proficient in leading teams, client communication, and utilizing advanced AI development tools and methodologies.

Highest-signal resume keywords

AI Solution DeliveryRAG ExpertiseProduction-Grade Code in Python, .NET/C#, Java, or Node.js/TypeScriptDeployment on Hyperscaler AI PlatformsClient-Facing Communication

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Hard Skills

RAG ExpertiseProduction-Grade CodeAI Workload DeploymentObservabilityEvaluation FrameworksSafety and SecurityNL2SQLGraph-Based RetrievalLLMOpsMulti-Step LLM Workflows

Soft Skills

Client-Facing CommunicationTeam LeadershipFeedback and Performance Management

Tools & Technologies

LangChainLangGraphAzure AI FoundryAWS BedrockGoogle Vertex AIGitHub CopilotCursorClaude CodeNeo4jRDF/SPARQL

Industry Keywords

AI EngineeringProduction AI SystemsLarge Regulated OrganizationsIntelligent Document ProcessingEmbedding Models

Tech Stack

Tools & technologies

AWSAzureJavaJavaScriptKubernetesNeo4j.NETNode.jsPythonTypeScript

About the role

Key responsibilities & impact

Work directly with clients: translate business problems into AI solutions, explain how the technology behaves (and its limits), and report progress clearly;
Support proposals and pre-sales for new AI engagements;
Lead AI engineers on engagements: if needed to have ability to set tasks, review work, give feedback, and support growth and performance;
Own delivery quality and the path from PoC to production;
Design end-to-end AI-powered experiences and the systems behind them: model selection, deployment patterns, infrastructure;
Architect and build RAG pipelines - chunking (semantic/recursive/hierarchical), embedding selection, retrieval, hybrid search, re-ranking, and evaluation;
Build agentic/multi-step workflows (LangChain, LangGraph, or equivalent): multi-agent designs, agent-to-agent protocols, and tool/API/MCP-style integration with enterprise systems;
Deploy on a hyperscaler AI platform (Azure AI Foundry, AWS Bedrock, Google Vertex AI, K8s based or equivalent);
Build for production: observability, evaluations, AI safety, bias/failure-mode handling, security, and cost control;
Write the production code yourself - hands-on;
Use AI development tools (GitHub Copilot, Cursor, Claude Code, etc.) as part of how you build and validate what they produce.

Requirements

What you’ll need

5+ years in software/AI engineering, with substantial hands-on delivery of production AI/LLM systems
Deep RAG expertise: chunking strategies, embedding models, vector databases, retrieval, re-ranking, and evaluation frameworks
Strong with agentic/multi-step LLM workflows and orchestration (LangChain, LangGraph, or equivalent) and tool/API integration
Able to build real software — production-grade code in at least one of Python, .NET/C#, Java, or Node.js/TypeScript (APIs, services, integrations)
Experience deploying AI workloads on at least one hyperscaler AI platform
Solid on production concerns: observability, evaluation, safety, security, cost
Daily, fluent use of AI development tools in your own work
Proven ability to take an AI solution from idea to production independently
Confident client-facing and team communication
Knowledge graphs and graph-based retrieval (Neo4j, RDF/SPARQL, GraphRAG)
NL2SQL, semantic layers, intelligent document processing
LLMOps / MLOps tooling and practices.
Nice to have: Broad/multi-stack traditional software-engineering background; Experience with large, regulated organisations (banks, insurers, government, utilities); Relevant certifications; Formal team-leadership/performance-management experience.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development