Python – AI/ML Engineer

Job Summary:

We are looking for a passionate and skilled Python – AI/ML Engineer who brings strong expertise in building AI/ML solutions, productionizing models, and contributing to end-to-end ML pipelines. The ideal candidate should possess deep knowledge of traditional and deep learning concepts, hands-on programming capabilities, experience in enterprise-grade software engineering, and a good understanding of MLOps practices.

Key Responsibilities:

• Design, develop, and deploy scalable machine learning models for classification, regression, NLP, and generative tasks.
• Build and optimize data transformation workflows using Python and Pandas.
• Lead AI/ML project pipelines from data ingestion to model deployment and monitoring.
• Implement model observability, monitoring for drift, and continuous model evaluation.
• Develop REST APIs and integrate ML models with production systems using frameworks like FastAPI.
• Participate in code reviews, write unit/integration tests, and ensure high code quality.
• Collaborate with cross-functional teams including Data Engineers, DevOps, and Product Managers.
• Stay current with the latest developments in AI, GenAI, ML frameworks, and tools. Use DevOps/MLOps tools to automate and manage model lifecycle processes.

Required Skills:

Programming & Python Ecosystem:

• Advanced proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
• Strong understanding of asynchronous programming, FastAPI, and concurrency

(Starlette).

• Deep understanding of multithreading, multiprocessing, and the Python GIL.
• Ability to write clean, efficient, and testable code.

Machine Learning & Deep Learning:

• Solid grasp of traditional ML concepts: classification, regression, overfitting/underfitting, regularization (L1/L2), multicollinearity.
• Experience with deep learning: RNNs, attention mechanisms, dropout, early stopping, loss functions (BCE, categorical cross entropy), diffusion models vs GANs.
• Familiarity with transfer learning and pre-trained model fine-tuning. MLOps:
• Understanding of ML pipeline design including model training, deployment, and monitoring.
• Experience in detecting and mitigating data drift and concept drift.
• Exposure to model observability, monitoring unstructured data drift, and automated drift alerts.

Software Engineering & DevOps:

• Strong skills in REST API development, integration testing, and CI/CD practices.
• Experience with containerization tools like Docker.
• Familiarity with cloud-based ML deployment (AWS/Azure) and logging frameworks. Hands-On Problem Solving & Data Engineering:
• Ability to perform data transformation and aggregation using Python/Pandas.
• Experience handling dataframes, joins, ranking, filtering, mapping, and custom logic for preprocessing tasks.

Nice-to-Have:

• Experience in GenAI and working with LLMs.
• Exposure to tools like MLflow, Kubeflow, Airflow, or similar MLOps platforms.
• Understanding of NLP, embeddings, and transformer-based models.
• Prior contributions to open-source ML tools or GitHub repositories.