About the Role :
We are looking for a hands-on Machine Learning Engineer to design, build, and deliver end-to-end machine learning products at scale.
In this role, you will work across the complete ML lifecycle—from problem framing and research to production deployment, monitoring, and continuous improvement. You will balance the development of new AI capabilities with the modernization and optimization of existing machine learning solutions.
You will collaborate closely with Data Scientists, Software Engineers, Product Managers, and business stakeholders to transform complex business problems into scalable, measurable AI solutions.
Key Responsibilities :
- Lead end-to-end machine learning projects, from scoping and data discovery through deployment, monitoring, and iteration.
- Design and deploy production-grade ML systems, including batch processing jobs, real-time APIs, and data and feature pipelines.
- Develop clean, reliable, fault-tolerant, and well-tested code using Python and SQL.
- Apply modern MLOps practices, including CI/CD, Docker, Kubernetes, model registries, and feature stores.
- Use AI-assisted development and coding tools to improve engineering productivity, code quality, testing, and development workflows.
- Select and apply appropriate machine learning approaches, including classical ML, deep learning, NLP, LLMs, RAG, and agentic AI systems.
- Work on use cases such as recommendations, search and ranking, forecasting, pricing, fraud detection, personalization, and supply chain optimization.
- Own feature engineering, experimentation, model evaluation, A/B testing, model monitoring, drift detection, and retraining strategies.
- Modernize legacy ML solutions and improve inference latency, cost efficiency, reproducibility, observability, and governance.
- Translate ambiguous business requirements into clearly defined machine learning problems and communicate technical trade-offs to stakeholders.
- Contribute to technical standards, ML platform improvements, and mentor junior engineers and data professionals.
Required Skills & Qualifications :
- Master's or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related quantitative discipline; equivalent practical experience will also be considered.
- Proven experience delivering machine learning products into production at scale.
- Strong understanding of data structures, algorithms, data modelling, and software architecture.
- Strong knowledge of the end-to-end ML lifecycle, including data discovery, feature engineering, model development, evaluation, deployment, and monitoring.
- Advanced Python programming skills with experience in libraries and tools such as Pandas, NumPy, Scikit-learn, and Jupyter.
- Strong SQL skills for data analysis and analytical workflows.
- Hands-on experience using AI-assisted coding tools or coding agents as part of the software development lifecycle.
- Experience with at least one major cloud platform; experience with Google Cloud Platform is preferred.
- Practical knowledge of Docker, Kubernetes, and CI/CD pipelines for machine learning workflows.
- Strong understanding of applied statistics, including hypothesis testing, sampling, experimentation, A/B testing, anomaly detection, predictive modelling, and regression analysis.
- Strong communication and stakeholder management skills, with the ability to manage multiple projects and work effectively across cross-functional teams.
Preferred Skills :
- Experience with agent frameworks and AI agent development platforms.
- Hands-on experience with PyTorch or TensorFlow, particularly for computer vision or multimodal AI use cases.
- Experience with distributed data processing technologies such as Spark, Beam, or Dataflow.
- Experience with streaming technologies such as Kafka or Pub/Sub.
- Knowledge of feature stores and experiment-tracking tools such as Feast, MLflow, Weights & Biases, or similar platforms.
- Experience working on machine learning use cases in retail, e-commerce, fintech, or other large-scale digital platforms.
- Contributions to open-source projects, published technical work, Kaggle projects, or a strong public technical portfolio.
What We Offer :
- Opportunity to work on modern AI and machine learning technologies.
- Exposure to complex, large-scale business and technology challenges.
- Collaborative environment with experienced engineering and data professionals.
- Opportunities for continuous learning, technical development, and career growth.
Equal Opportunity :
We are an equal opportunity employer and are committed to building an inclusive and respectful workplace. Employment decisions are based on skills, qualifications, experience, and business requirements, without discrimination as protected by applicable laws.