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.