Lead Machine Learning Engineer

We are looking for a Lead Machine Learning Engineer to build and lead the next generation of ML capabilities at OpenFX. This is a highly hands-on role where you will work on deep learning, NLP/LLMs, predictive modelling, intelligent automation, anomaly detection, and ML systems at scale. You will work closely with engineering, product, data, and security teams to take ML models from research and experimentation to production.

Responsibilities:

• Lead the design, development, and deployment of production-grade machine learning systems.
• Build and optimise models using deep learning, NLP, LLMs, and advanced ML techniques.
• Work on problems involving transaction intelligence, risk detection, fraud/anomaly detection, classification, prediction, and automation.
• Develop and productionize models using large-scale transactional and behavioural datasets.
• Design ML pipelines covering data preparation, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.
• Experiment with state-of-the-art architectures and research techniques and translate them into production solutions.
• Work with LLMs, embeddings, transformers, RAG, fine-tuning, and model evaluation, as needed.
• Optimise models for latency, scalability, accuracy, reliability, and cost.
• Establish best practices around ML engineering, experimentation, model versioning, monitoring, and reproducibility.
• Mentor engineers and contribute to building the ML engineering culture at OpenFX.
• Partner with product and engineering leaders to identify high-impact opportunities where ML can create business value.

Requirements:

• 6+ years of experience in machine learning / applied ML / AI engineering.
• Strong hands-on experience with deep learning.
• Strong understanding and practical experience with NLP and modern language models.
• Experience building and deploying ML models in production, not just research/prototyping.
• Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Strong fundamentals in machine learning, deep learning, probability and statistics, optimisation, algorithms, and data structures.
• Experience with Transformers, embeddings, LLMs, RAG, fine-tuning, or related GenAI technologies is highly desirable.
• Experience working with large datasets and distributed data/compute environments.
• Strong understanding of model evaluation, experimentation, performance optimisation, and monitoring.
• Ability to independently take ambiguous ML problems from problem definition to research implementation and production.
• Strong system design and software engineering fundamentals.

Preferred Background:

• We are particularly interested in candidates from Tier-1 engineering colleges and leading research institutes, including candidates with strong academic/research backgrounds in computer science, artificial intelligence, machine learning, data science, mathematics, statistics, and computational sciences.
• Candidates with strong publications, research projects, open-source contributions, competitive programming backgrounds, or significant ML systems built at scale will be highly valued.