Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to join our AI team as the technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle — from model design through deployment, optimization, and ongoing performance in production — and contributes to the technical direction of the AI platform.

Key Responsibilities

• Architect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day one.
• Build and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scale.
• Own technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainability.
• Design and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applications.
• Implement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines.
• Continuously optimize model performance, inference latency, cost efficiency, and reliability across live systems.
• Collaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutions.
• Mentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the team.
• Contribute to strategic decisions around data architecture, AI infrastructure, and cloud platform direction.
• Work with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimization.

Requirements

• 7 to 15 or more years of experience in software engineering, data science, or ML engineering.
• Strong background in product companies, scale-ups, or enterprise AI platforms.
• Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work.
• Comfortable owning systems end-to-end from data through model through deployment through monitoring.
• Product-first engineering approach, not research-only profiles.
• Advanced Python engineering skills with strong systems thinking and a focus on production quality.
• Comfortable with fast iteration cycles and deploying models into live environments.
• Ability to work directly and confidently with stakeholders and product owners.
• Fintech or financial services experience is an advantage.

Technical Skills

• Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)
• LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelines
• MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks
• Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
• Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures

Why Join Us?

• Work with one of the world’s leading financial derivatives institutions.
• Competitive salary plus performance-based incentives.
• Access to a dynamic, international, and fast-growing environment.
• Strong opportunities for career progression within a global financial group.
• Be part of a business committed to innovation, excellence, and long-term growth.