Collinson

Collinson is the global, privately-owned company dedicated to helping the world to travel with ease and confidence.

We use our expertise and products to craft customer experiences which enable some of the world’s best-known brands to acquire, engage and retain the most demanding and choice-rich customers. Our unique expertise and insight into high earning, frequent travellers allow us to create products and solutions for our clients that inspire greater customer engagement to drive more profitable relationships, enrich their travel experiences, protect what matters and assist in in times of need.

While specialising in Financial Services, Travel and Retail, we also support clients in multiple sectors. We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and banks, with clients including Accor Hotels, Air France KLM, American Express, British Airways, Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa and Vhi.

We take our 30 years’ experience working with these kinds of household names in over 170 countries and help our clients to deliver the smarter experiences it takes to differentiate their propositions and help them win deeper devotion with their customers. 

Collinson is a privately-owned entrepreneurial business with 2,000 passionate people working in 20 locations worldwide.  Our solutions include Priority Pass, the world’s best known airport experiences programme, while we are also the trusted partner behind many of the leading financial services, airline and hotel brand’s reward programmes and loyalty initiatives. 

Purpose of the job

As a Machine Learning (ML) Engineer at Collinson, you will play a critical role in driving the development of cloud-based machine learning pipelines for data-driven products and services. Your responsibilities will include collecting data from various business units and leveraging a centralized data platform to productionize analytics and machine learning workflows. Additionally, you will be expected to provide analytical expertise across the Collinson group, ensuring the implementation of cloud-based solutions that meet the needs of both internal and external clients from across Collinson's global footprint. 

As an innovator, you will be tasked with bringing fresh ideas to the team and continuously exploring new and modern engineering frameworks to enhance the overall offerings of the Collinson group. A key aspect of this role will also be to collaborate with the data platform team to integrate with the ML platform, and to support the growth and development of the team's ML skillset. 

Also, it will be essential for you to be able to identify and resolve issues that arise, ensuring the quality and quantity of work produced by the team is always maximized. This will require a combination of technical expertise, problem-solving skills, and the ability to effectively communicate and collaborate with stakeholders.  

Senior ML Engineer Job Description

The Senior ML Engineer will own the end-to-end design, development, and deployment of advanced ML and AI platforms, driving the architecture and technical direction for high-performance, scalable machine learning solutions. The role demands extensive hands-on expertise in building machine learning platforms, leveraging AWS SageMaker, Python-based frameworks, and parallel computing for efficient large-scale data processing.

Key Responsibilities:

• Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms.

• Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies.

• Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models.

• Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems.

• Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms.

• Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization.

• Stay abreast of latest advancements in ML and AI technologies, proactively applying new techniques and tools to deliver superior outcomes.

Day-to-Day Activities:

• Design, build, and maintain scalable ML platform infrastructure and tooling.

• Develop optimized machine learning models, leveraging advanced supervised and unsupervised techniques.

• Rapidly prototype and iterate on proof-of-concepts and transition successful prototypes into enterprise-grade solutions.

• Conduct comprehensive reviews and present findings clearly to technical and non-technical stakeholders.

• Continuously perform horizon scanning and research emerging ML trends, tools, and methodologies.

Knowledge and Skills:

Essential:

• Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker.

• Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn).

• Extensive hands-on experience with parallel computing frameworks and distributed processing.

• Proficient in SQL, ETL, data warehousing, and data modeling techniques.

• Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies.

• Proven capability of deploying machine learning models into production at scale.

• Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git).

Preferred:

• Experience with Kubernetes orchestration systems.

• Familiarity with Snowflake data warehousing and NoSQL databases like MongoDB.

• Hands-on experience with messaging systems (e.g., Kafka).

• Exposure to distributed computing frameworks such as Hadoop, Spark, and Ray.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related fields. A Ph.D. is advantageous.

• 7+ years of proven hands-on experience building ML/AI platforms, especially involving AWS SageMaker and distributed computing frameworks.

Person Specification:

• Proactive, self-driven innovator who thrives in fast-paced environments.

• Exceptional technical leadership skills combined with strong attention to detail.

• Outstanding communication skills, capable of clearly articulating technical concepts to diverse audiences.

• Ability to effectively bridge business and technical teams, ensuring alignment on objectives and outcomes.