Senior Machine Learning Engineer

About Inception42

Inception42, part of the G42 group, leads AI innovation in the region, creating both domain-specific and broad industry products grounded in extensive research and development. Operating as the central intelligence layer inside G42, Inception42 transforms data and compute resources into practical AI solutions that impact society positively.

Role Overview

We seek a Senior Machine Learning Engineer dedicated to developing advanced ML solutions into dependable, scalable systems. You will manage the entire engineering lifecycle, from data handling and experimentation to training, deployment, and ongoing monitoring. Collaboration with applied scientists, MLOps, software engineers, and product teams is vital. This role requires balancing real-world constraints such as latency, reliability, security, governance, and cost.

Key Responsibilities

• Create, expand, and maintain production machine learning systems across all phases: data prep, feature engineering, training, evaluation, deployment, and observability.
• Convert research models into maintainable production features, ensuring readiness through addressing data quality, system performance, and operational stability.
• Develop reusable pipelines supporting training and evaluation with systematic experiment tracking, version control of models and datasets, benchmarking, and reproducibility.
• Build both batch and online inference pipelines, APIs, and feature workflows that align with product criteria for latency, throughput, availability, and cost efficiency.
• Refine models and systems to maximize accuracy, robustness, computational efficiency, and scalability based on profiling and live workload data.
• Implement monitoring for model quality, data integrity, latency, throughput, availability, and overall system health, troubleshooting full-stack issues as they arise.
• Work closely with MLOps, platform, and software engineering teams to enable CI/CD, containerization, orchestration, release strategies, rollbacks, and production support.
• Integrate security, privacy, governance, audit, and compliance frameworks into workflows for data, models, and deployment from inception.
• Lead architectural and technical decisions concerning ML components, balancing product demands with maintainability, reliability, performance, and delivery pace.
• Enhance engineering standards through design and code reviews, thorough documentation, standard reuse, and mentoring less-experienced engineers.

Qualifications

• Solid software engineering background and proficiency in Python, with experience producing tested, maintainable systems rather than just prototypes or isolated scripts.
• Practical experience deploying and maintaining production-grade machine learning systems, owning their performance and reliability post-deployment.
• Deep understanding of machine learning concepts, with specialization in deep learning, NLP, computer vision, recommendation algorithms, or generative AI preferred.
• Familiarity with ML frameworks like PyTorch, TensorFlow, or scikit-learn, and the ability to choose appropriate tools based on problem specifics.
• Knowledge of data workflows including SQL, data validation, feature engineering, distributed processing, and efficient large dataset management.
• Hands-on experience with model serving solutions, APIs, containerization, orchestration, CI/CD pipelines, and cloud infrastructures in production settings.
• Strong debugging capabilities and systems perspective spanning data inputs, model responses, application layers, services, and infrastructure.
• Effective communication skills, sound product intuition, and an ability to navigate ambiguity with pragmatic decision-making and cross-team collaboration.

Desirable Experience

• Work with large language models, retrieval-augmented generation, autonomous agent systems, evaluation frameworks, guardrails, or high-efficiency inference systems.
• Experience using Azure Machine Learning, Azure AI Foundry, Databricks, AKS, or similar cloud-native AI and data environments.
• Expertise in distributed training, GPU optimization, feature store management, vector search technologies, or large-scale serving infrastructures.
• Contributions to reusable ML platforms, open source projects, applied research, or development of engineering standards that elevate practices beyond individual projects.