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AI / Machine Learning Engineer

HybridFull-time

Develop and deploy AI/ML models that solve real-world problems. Work on cutting-edge projects from research to production, building intelligent systems that drive business value.

Responsibilities

  • Design and implement machine learning models for various applications
  • Preprocess and analyze data for ML applications
  • Deploy ML models to production environments with proper monitoring
  • Optimize model performance, accuracy, and efficiency
  • Collaborate with data scientists, engineers, and product teams
  • Stay updated with latest AI/ML research and trends
  • Document models, experiments, and best practices
  • Participate in code reviews and maintain ML codebase quality

Required Skills

  • Strong foundation in machine learning and deep learning
  • Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn)
  • Understanding of data preprocessing and feature engineering
  • Experience with model evaluation and validation techniques
  • Knowledge of statistics and probability
  • Familiarity with version control (Git) and software development practices

Nice-to-Have Skills

  • Experience with NLP or computer vision applications
  • Knowledge of MLOps practices and tools
  • Familiarity with cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI)
  • Experience with model deployment and serving (FastAPI, Flask, TensorFlow Serving)
  • Research publications or contributions to ML community
  • Understanding of model interpretability and explainability

What Success Looks Like

  • Develop models that meet accuracy and performance requirements
  • Successfully deploy models to production with proper monitoring
  • Contribute to ML best practices and documentation
  • Stay current with ML research and apply new techniques effectively
  • Collaborate effectively with cross-functional teams
  • Deliver ML solutions that drive measurable business value

Tools and Technologies

  • • Languages: Python, R
  • • ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM
  • • Data Processing: Pandas, NumPy, Spark, Dask
  • • Deployment: Docker, Kubernetes, FastAPI, Flask, TensorFlow Serving
  • • Cloud ML: AWS SageMaker, Azure ML, GCP Vertex AI
  • • MLOps: MLflow, Weights & Biases, Kubeflow

Growth Opportunities

  • Advance to Senior ML Engineer or ML Architect
  • Lead ML research initiatives and projects
  • Specialize in specific AI domains (NLP, CV, Reinforcement Learning, etc.)
  • Present at conferences and publish research papers
  • Build ML platform and infrastructure
  • Mentor junior ML engineers and data scientists

Ready to Apply?

Send us your resume and a brief note about why you're interested in this role. We'd love to hear from you!

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