Sr. AI Engineer

End-to-End ML Pipelines: Big data processing, Design, develop, and deploy scalable machine learning models (Supervised, Unsupervised, and Deep Learning).

    Advanced Data Processing: Architect robust data preprocessing workflows to handle large-scale structured and unstructured datasets, ensuring high data quality and integrity.

    AI Innovation: Stay at the forefront of AI research (Generative AI, LLMs, NLP, or Computer Vision) and apply cutting-edge techniques to improve existing products.

    Feature Engineering: Collaborate with Data Engineers to create reusable feature stores and optimize ETL/ELT processes for ML readiness.

    Model Governance: Implement MLOps best practices, including model versioning (MLflow), experiment tracking, and monitoring for data drift and performance decay.

    Strategic Leadership: Translate ambiguous business problems into technical roadmaps and present actionable insights to executive stakeholders.

What you need to succeed 

    Programming: Mastery of Python (Pandas, NumPy, Scikit-learn) and proficiency in SQL for complex data extraction.

    Machine Learning: Deep expertise in classical ML (XGBoost, Random Forest) and Deep Learning frameworks (TensorFlow or PyTorch).

    Data Engineering: Strong experience in data processing at scale (using PySpark, Dask, or specialized SQL window functions).

    Deployment: Experience containerizing models with Docker/Kubernetes and deploying via APIs (FastAPI/Flask).

    Cloud Platforms: Hands-on experience with AI/ML services in Azure (Azure ML), AWS (SageMaker), or GCP (Vertex AI).

One Last thing

    Mentorship: Proven track record of guiding junior and mid-level Data Scientists through technical hurdles.

    Analytical Storytelling: The ability to explain the "why" behind a model's prediction to non-technical business leaders.

    Resilience: Comfort with the iterative nature of data science—knowing when to pivot an approach based on experiment results.