Python AI / ML Developer (LOCAL CANDIDATES ONLY)

Job Overview

We are seeking a talented Python AI/ML Developer to design, implement, and optimize machine learning models and AI systems. You will work closely with data engineers, researchers, and product teams to translate business problems into scalable ML solutions, including data processing pipelines, model development, evaluation, deployment, and monitoring.

Responsibilities:

• Develop, train, evaluate, and deploy scalable ML models and AI features using Python and related libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
• Design and maintain end-to-end data pipelines for feature extraction, cleaning, transformation, and storage.
• Experiment with model architectures, hyperparameters, and feature engineering to improve performance and robustness.
• Collaborate with data engineers to ensure data quality, lineage, and reproducibility; implement CI/CD for ML workflows.
• Write clean, maintainable, and well-documented code; create unit and integration tests for ML components.
• Build and monitor model deployment platforms (APIs, microservices, batch/real-time inference) and ensure low latency, scalability, and reliability.
• Implement model monitoring, drift detection, and automated retraining strategies; set up dashboards and alerts.
• Collaborate with product managers and stakeholders to translate requirements into ML solutions and measurable outcomes.
• Stay up-to-date with the latest research and best practices in AI/ML, and contribute to internal knowledge sharing.

Qualifications:

• Bachelor s or Master s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (or equivalent practical experience).
• Proficiency in Python and scientific computing stack (NumPy, pandas, SciPy, scikit-learn).
• Experience with one or more deep learning frameworks (TensorFlow, PyTorch, Keras) and model deployment (TorchServe, TensorFlow Serving, FastAPI, Flask).
• Familiarity with ML workflow tooling (MLflow, Kubeflow, Airflow, Luigi) and versioning/data lineage concepts.
• Strong understanding of machine learning fundamentals: supervised/unsupervised learning, model evaluation, bias/variance, feature engineering, regularization, hyperparameter tuning.
• Experience with data processing and ETL/ELT pipelines, SQL, and working with relational/noSQL databases.
• Knowledge of cloud platforms (AWS, Google Cloud Platform, Azure) and containerization (Docker, Kubernetes) is a plus.
• Experience with MLOps practices: monitoring, logging, model retraining, A/B testing, and rollback strategies.
• Excellent problem-solving skills, strong communication, and ability to work in cross-functional teams.
• Optional but desirable: experience with NLP, computer vision, time-series analysis, or reinforcement learning.