Job Description

Role & responsibilities Please Note - This is not a walk in interview only shortlisted candidates will be called. Your key responsibilities 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 modeling, A/B testing, time series, and experimentation. Proficient in NLP, deep learning , and working with transformer-based LLMs . Experience with MLOps practices — CI/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