Senior AI Engineer / Senior Machine Learning Engineer
Ac Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Ac. EG-Cairo · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and put machine-learning solutions into production, then assess and improve their performance
- Create applications powered by generative AI and large language models, including retrieval pipelines and knowledge bases
- Develop agents that use tools, manage state and carry out multi-step tasks with orchestration frameworks
- Set up evaluation and monitoring for machine-learning, retrieval and agent systems
- Turn technical and business challenges into practical AI solutions with engineering, product and domain teams
What you bring
- Three to six years in data science, machine learning, AI engineering or a related field
- Strong grounding in machine learning and data science, plus Python and common AI/ML libraries
- Production experience developing and deploying AI or machine-learning systems
- Practical generative AI and LLM work, with experience in retrieval, embeddings or vector databases
- Experience with agentic AI frameworks such as LangGraph or LangChain, and multi-step workflow concepts
Who this fits
This senior role suits an engineer who can work across the AI lifecycle, from data exploration and model development through deployment and ongoing improvement. The position is based in Cairo, Egypt, and involves collaboration across a multinational environment.
From the employer
Position:
Senior AI Engineer / Senior Machine Learning EngineerJob Description:
Position Overview
As a AI Engineer / Senior Machine Learning Engineer – GenAI & Agentic AI at Arrow Electronics, you will play a key role in developing and deploying intelligent AI solutions that address complex business and engineering challenges.
The role is built on a strong foundation in machine learning and data science, with a strong focus on Generative AI, Large Language Models (LLMs), RAG, knowledge bases, and agentic AI systems.
You will work across the full AI lifecycle, from data exploration and model development to production deployment, evaluation, optimization, and continuous improvement. You will collaborate with software engineers, data engineers, product teams, and domain experts across a multinational environment.
Key Responsibilities
- Design, develop, evaluate, and deploy machine learning and AI solutions for real-world problems.
- Apply strong ML and data science fundamentals and model evaluation.
- Develop Generative AI and LLM-powered applications using modern foundation models.
- Design and implement RAG pipelines, knowledge bases, semantic/hybrid search, and vector-based retrieval systems.
- Develop agentic AI and multi-step workflows using frameworks such as LangGraph and LangChain.
- Build agents capable of tool usage, reasoning, routing, state management, and multi-step task execution.
- Develop evaluation and monitoring approaches for ML, LLM, RAG, and agentic AI systems.
- Collaborate with technical and business stakeholders to translate complex problems into practical AI solutions.
Qualifications
Required
- 3–6 years of professional experience in data science, machine learning, AI engineering, or a related field.
- Strong machine learning and data science fundamentals are mandatory.
- Strong proficiency in Python and common ML/AI libraries.
- Hands-on experience developing and deploying ML/AI solutions in production.
- Practical experience with Generative AI and Large Language Models (LLMs).
- Experience with RAG, embeddings, vector databases, semantic search, or knowledge-based AI systems.
- Hands-on experience with LLM orchestration and agentic AI frameworks, preferably LangGraph and/or LangChain.
- Understanding of agentic architectures, tool calling, workflow orchestration, state management, and multi-step AI workflows.
- Strong analytical, problem-solving, and communication skills.
Preferred / Plus
- Experience building production-grade AI agents or multi-agent systems.
- Experience with LLM/RAG evaluation, observability, guardrails, and reliability techniques.
- Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, or Google Vertex AI.
- Experience with Elasticsearch, OpenSearch, Solr, FAISS, pgvector, Pinecone, Milvus, or similar technologies.