Python

Job Title: Python AI/ML Engineer

Location: Gurugram

Experience: 5–8 years

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.Role & responsibilities

Preferred candidate profile