Your key responsibilities (18+ years Only)
- Proven ability to design, build, and deploy end-to-end AI/ML systems in production.
- Expertise in data science, including statistical analysis, experimentation, and data storytelling.
- Experienced in working with large-scale, real-world datasets for model training and analysis.
- Comfortable navigating urgency, ambiguity, and fast-changing priorities. Skilled at solving complex ML problems independently, from idea to implementation.
- Strong leadership experience building and guiding high-performing AI teams. Hands-on with deep learning, NLP, LLMs, and classical ML techniques. Fluent in model experimentation, tuning, optimisation, and evaluation at scale.
- Solid software engineering background and comfort working with data and full-stack teams.
- Experience with cloud platforms (GCP, AWS, Azure) and production-grade ML pipelines.
- Bias for action willing to jump into code, data, or ops to ship impactful AI products.
Your skills and experience
- PhD in Computer Science, Data Science, Machine Learning, AI, or a related field.
- Strong programming skills in Python (preferred), with experience in Java, Scala, or Go a plus.
- Deep expertise with ML frameworks like TensorFlow, PyTorch, Scikit-learn.
- Experience with large-scale datasets, distributed data processing (e.g. Spark, Beam, Airflow).
- Solid foundation in data science: statistical modelling, A/B testing, time series, and experimentation.
- Proficient in NLP, deep learning, and working with transformer-based LLMs.
- Experience with MLOps practicesCI/CD, model serving, monitoring, and lifecycle management.
- Hands-on with cloud platforms (GCP, AWS, Azure) and tools like Vertex AI, SageMaker, or Databricks.
- Strong grasp of API design, system integration, and delivering AI features in full-stack products.
- Comfortable working with SQL/NoSQL and data warehouses like BigQuery or Snowflake.
- Familiar with ethical AI, model explainability, and secure, privacy-aware AI development.
