AI & Machine Learning Engineer

Your key responsibilities

You will work with a wide variety of clients to deliver the latest data science and big data technologies. Your teams will design and build scalable solutions that unify, enrich, and derive insights from varied data sources across a broad technology landscape. You will help our clients navigate the complex world of modern data science, analytics, and software engineering. We'll look to you to provide guidance and perform technical development tasks to ensure data science solutions are properly engineered and maintained to support the ongoing business needs of our clients.

You will be joining a dynamic and interdisciplinary team of scientists and engineers who love to tackle the most challenging computational problems for our clients. We love to think creatively, build applications efficiently, and collaborate in both the ideation of solutions and the pursuit of new opportunities. Many on our team have advanced academic degrees or equivalent experience in industry.

Skills and attributes for success

This role will work to deliver tech at speed, innovate at scale and put humans at the center. Provide technical guidance and share knowledge with team members with diverse skills and backgrounds. Consistently deliver quality client services focusing on more complex, judgmental and/or specialized issues surrounding emerging technology. Demonstrate technical capabilities and professional knowledge.

To qualify for the role, you must have

• Bachelor's degree and 6-10 years of full-time working experience in AI, Data Science, and/or Machine Learning
• 2-4 years of experience directly managing technical teams
• Extensive experience in telecommunications, media, or technology sectors, with a strong understanding of industry trends.
• Strong skills in Python.
• Experience using Generative AI models and frameworks e.g. OpenAI family, open source LLMs, Dall-e, LlamaIndex, Langchain, Retrieval Augmented Generation (RAG).
• Experience working with popular ML packages such as scikit-learn, Pytorch and ONNX, or related ML libraries.
• Extensive experience using DevOps tools like GIT, Azure Devops and Agile tools such as Jira to develop and deploy analytical solutions with multiple features, pipelines, and releases.
• Proven ability to design and implement machine learning solutions that enhance network performance and customer support
• ) and their implications for data science.
• Experience analyzing large-scale datasets from telecommunications networks, including call detail records and performance metrics.
• Knowledge of streaming technologies, including analytics and content recommendation systems.
• Experience with multi-modal models, including vision-language models.
• Understanding of customer segmentation, churn prediction, and recommendation systems tailored to media and telecommunications.
• Knowledge of regulatory and compliance considerations specific to the banking and financial sector.
• Experience with real-time data processing frameworks (e.g., Apache Kafka).
• Familiarity with cloud-based telecommunications solutions (e.g., AWS, Azure).
• Experience with network optimization algorithms and techniques.
• Knowledge of emerging technologies such as edge computing and IoT.
• A solid understanding of Machine Learning (ML) workflows including ingesting, analysing, transforming data and evaluating results to make meaningful predictions.
• Experience with MLOps methods and platforms such as MLFlow.
• Experience with CI/CD and test-driven development.
• Experience designing, building, and maintaining ML models, frameworks, and pipelines.
• Experience designing and deploying end to end ML workflows on at least one major cloud computing platform.
• Understanding of data structures, data modelling and software engineering best practices.
• Proficiency using data manipulation tools and libraries such as SQL, Pandas, and Spark.

Ideally, you'll also have

• A deep understanding of and ability to teach concepts, tools, features, functions, and benefits of different approaches to apply them.
• Master's degree Computer Science, Mathematics, Physical Sciences, or other quantitative field.
• Experience working with diverse teams to deliver complex solutions.
• Strong skills in languages beyond Python: R, JavaScript, Java, C++, C.
• Experience fine-tuning Generative AI models.