Machine Learning Specialist

• *Principal Machine Learning Engineer** • *Hybrid (3 days per week in-office)** • *About the Role**

We are looking for an exceptional • *Principal Machine Learning Engineer**

to play a pivotal leadership role within our Intelligent Data division. This role is designed for a • *world‑class engineer\*\*\*\*\*someone who began their career in software engineering and evolved into the data, AI, and machine learning domain through hands-on engineering of high‑performance systems\*\*\***

. You will provide both

• *technical leadership**

and • *strategic direction**

to a team building cutting‑edge, real‑time AI applications. Our work is distinguished by engineering excellence, advanced use of LLMs, and deep investment in scalable data platforms with governance and quality at their core. • *Examples of Our Work** • **Real-Time Fraud Prevention:**

Built a real-time behavioural and financial data ingestion system for a major bank that detects—and actively blocks—fraudulent transactions as they occur. • **Advanced Vehicle Telemetry:**

Engineered large-scale, event-driven pipelines for in‑vehicle telemetry, reducing hijacking false alarms and significantly improving emergency response times. • **LLM Leadership:**

With more than five years’ production experience with Large Language Models (including fine-tuning GPT‑3 in 2020), we run one of the most advanced AI engineering practices in the region. The successful candidate will help guide our ML strategy, shape the team’s technical direction, and serve as a public thought leader representing Synthesis.

• *Key Responsibilities** • *Technical & Architectural Leadership** • Act as the senior technical authority on the architecture and design of complex, real‑time ML and AI systems. • Solve the most challenging engineering problems; make architecture‑defining decisions. • Provide deep expertise in data streaming and event‑driven systems (critical to our work). • Evaluate, adopt, and champion advanced technologies, frameworks, and tools. • *Strategic Contribution** • Collaborate with leadership to define and refine the organisation’s machine learning strategy. • Research emerging trends (LLMs, generative AI, agentic systems) and apply them to our product and platform roadmap. • Maintain our competitive edge by driving strategic innovation. • *Team Leadership & Mentorship** • Coach, mentor, and elevate a high‑performing engineering and data team. • Perform rigorous code reviews and enforce high standards of quality and reliability. • Build a culture of collaboration, technical excellence, and continuous learning. • *Thought Leadership & Communication** • Represent the Intelligent Data team as a confident communicator and evangelist. • Deliver conference talks, publish content, and participate in community discussions. • Communicate complex technical concepts clearly to all audiences—technical and non‑technical. • *Required Skills & Experience** • *Non-Negotiable Requirements** • *1.

Machine Learning

Engineer at Heart**

This is a

• *hands-on engineering leadership role**

, not a theoretical data science role. You must have a demonstrable track record of building

• *end‑to‑end ML and AI systems**

, including data pipelines, deployment stacks, and production-grade architectures. • *2. Data Streaming & Platform Expertise**

Deep experience with

• Kafka • Flink • Beam • And a strong understanding of modern data platform design. • *3. Multi-Cloud Mastery**

Hands-on experience architecting solutions across multiple clouds. • *Priority:** • AWS (primary) • GCP • Azure • *4.

Technical

Versatility**

Strong experience across several programming languages such as: • Python • Go • Java • C# • *5.

Inspirational

Leadership**

A proven ability to mentor, motivate, and raise the technical bar for engineering teams. • *6.

Exceptional

Communication**

Capable of presenting complex ideas with clarity and confidence. A history of conference talks, blog posts, or public content is a significant advantage.

• *Technologies & Concepts** • **Cloud:**

AWS (primary), GCP, Azure • **Key Services:**

Lambda, S3, RDS, DynamoDB, VPC • **Streaming:**

Kafka, Flink, Beam • **Programming:**

Python, Go, Java, C#, JavaScript • **Architecture:**

Event‑Driven Architecture, Microservices, Docker, Kubernetes • **Data/ML:**