Python - AI/ML Engineer

2 weeks ago

Gurgaon, Haryana, India Mindsprint Full time US$ 1,50,000 - US$ 2,00,000 per year

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.

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