AI Engineer

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale.

Responsibilities:

• Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns.
• Build and productionize RAG pipelines, prompt-based applications, and LLM integrations across providers.
• Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring.
• Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers.
• Implement guardrails, prompt-injection defenses, and evaluation frameworks to keep AI systems safe and reliable in production.
• Write clean, tested, production-grade code and participate actively in code and design reviews.
• Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions.
• Troubleshoot and optimize AI systems for accuracy, latency, and cost in production.

Requirements:

• 8-12 years of hands-on software engineering experience, with a strong, unbroken technical track record.
• Hands-on experience building and shipping AI/ML systems in production, not just POCs.
• Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI)
• Agents SDK, Bedrock Agents/Strands, or Semantic Kernel.
• Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.
• Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices.
• Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation.
• Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases.
• Understanding of LLM safety and reliability practices: guardrails, prompt-injection defenses, and observability.

Preferred Qualifications:

• Experience with fine-tuning, model distillation, or self-hosted/open-weight model deployment.
• Experience integrating enterprise systems such as Salesforce, Microsoft Dynamics, ServiceNow, or SAP.
• Experience with multilingual (e. g., Arabic/English) conversational or agentic AI experiences.
• Certifications: AWS ML Specialty, AWS Solutions Architect Associate/Professional, or Azure Data Scientist Associate.
• Open-source contributions, technical writing, or conference speaking in the AI space.