Senior AI and Machine Learning Engineer

Company Description CGNI Intelligence is an Enterprise Intelligence startup helping organizations close the gap between what they know and what they can use for real-time decision-making. We work within existing technologies and processes to turn data, context, and human expertise into better decisions, more effective execution, and measurable business outcomes. We develop in-house products and deliver client services across industries, locally and internationally. Our work combines reusable engineering capabilities with solutions shaped around each enterprise’s needs. We start with business problems and apply AI where there is clear business, technical, economic, or risk justification. Our approach emphasizes understanding the enterprise, making effective use of existing capabilities, sustainable AI economics, and clear ownership and governance. We also help client teams build the capability to operate and improve their solutions independently. We are building our own operations around accountable people working alongside AI and Digital Workers—AI-enabled systems that perform defined work under human accountability. Role Description Building an AI demo is one thing. Making AI work in the real enterprise is another. We are looking for a Senior AI & ML Engineer to design, build, and deploy AI systems that solve complex enterprise problems and deliver measurable business impact. This full-time, on-site role is based in Amman, Jordan, with work spanning in-house development and engagements for local and international clients. You will work across LLMs and Retrieval-Augmented Generation (RAG), agentic AI and multi-agent systems, and applied machine learning. Assignments may range from technical assessment and advisory contributions to targeted integration, solution delivery, and ongoing improvement. Working closely with business stakeholders, engineers, and clients, you will translate requirements into reliable systems integrated with existing technologies and workflows. Across industries, you will encounter different business processes, data environments, and operational constraints—and use that understanding to assess readiness and help determine where AI is appropriate. Joining at this stage gives you the opportunity to shape technical approaches, establish practical engineering standards, and develop capabilities that improve through successive releases and real-world use. You will have meaningful ownership of your AI/ML engineering scope and a direct connection between what you build and the results it creates. You will collaborate across enterprise understanding, data engineering, platform engineering, and delivery coordination as the team grows, with responsibilities and dependencies defined for each assignment. We value engineers who make sound technical decisions, test assumptions quickly, and explain when a simpler approach will serve the business better. Key Responsibilities Own AI/ML engineering work across in-house products and client engagements, taking responsibility for the agreed scope from feasibility and design through evaluation, deployment, and improvement.Design and build LLM applications, RAG pipelines, and agentic systems that use enterprise data, knowledge, and tools, with defined execution boundaries and appropriate human oversight.Perform data analysis and feature engineering, train or adapt models where appropriate, and collaborate on the data pipelines, quality, and access requirements that AI systems depend on.Establish evaluation methods and test harnesses to assess model quality, retrieval performance, agent behavior, and end-to-end system reliability against agreed acceptance criteria.Collaborate on production readiness, including monitoring, scalability, failure handling, security, and governance. Make system behavior, cost, and drift observable, with clear ways to intervene when AI behaves unexpectedly.Assess tradeoffs between quality, latency, cost per task, projected operating costs, and business value, and contribute evidence of achieved outcomes after deployment.Research and prototype new approaches, evolve in-house AI capabilities through successive releases, and use evidence to guide production adoption, reuse, and integration.Support and mentor fellow engineers, contribute to technical standards and reusable patterns, and help client teams maintain and evolve deployed solutions through documentation, handover, and practical capability transfer. Qualifications We are looking for depth in production AI engineering, supported by strong fundamentals and the ability to learn across the wider stack. At least 3 years of hands-on AI/ML experience, including at least 2 years working with Generative AI and LLMs.Demonstrated ownership of designing, building, deploying, and supporting AI/ML solutions in production, including evaluating performance and addressing failures in use.Strong Python skills and solid foundations in computer science, algorithms, software engineering, statistics, and machine learning.Hands-on experience with LLM applications and RAG, including embeddings, retrieval, context construction, and evaluation.Practical experience building agentic systems with tool integration, defined execution boundaries, and human approval or intervention where needed; multi-agent experience is an advantage.Proficiency with relevant ML frameworks and tools, such as PyTorch, TensorFlow, or scikit-learn.Experience integrating AI systems with APIs, data pipelines, and cloud platforms such as AWS, Azure, or GCP.An engineering mindset that considers evaluation, reliability, scalability, cost, operational ownership, and what happens after deployment.Ability to work with non-technical stakeholders, translate business problems into technical requirements, and communicate results, limitations, and tradeoffs clearly.Willingness to take initiative, work through uncertainty, and share knowledge as the team grows.Bachelor’s, master’s degree prefered, in Computer Science, Data Science, AI, or a related field, or equivalent practical experience. How to Apply Send your resume to info@cgni.ai. If available, include a brief example of an AI system you helped bring into production, outlining your contribution and what you learned. Please exclude confidential information.