Why This Role?
Tired of ML projects that never leave the notebook? Here, you'll own the full journey: from raw data to a model running in production and serving real users. You'll work on real-world AI problems, learn across the entire ML lifecycle, and grow alongside Data Scientists, Data Engineers, Software Engineers, and Product Managers.
If you're an early- to mid-career ML engineer who wants hands-on impact and fast growth, this is for you.
What You'll Do
Build and improve models
Design, train, and evaluate ML models for real business and technical problems.
Clean and analyze datasets, engineer features, and choose the right algorithms.
Tune and experiment your way to better performance
Take models to production
Build reproducible ML pipelines and workflows.
Deploy models across development, staging, and production environments.
Integrate models into applications through APIs and backend systems
Keep them healthy
Monitor model performance, data quality, and system reliability.
Troubleshoot model, data, and production issues.
Apply solid validation, testing, and version-control practices
Collaborate and grow
Document your experiments, assumptions, and results so the team can build on them.
Stay on top of emerging ML, deep learning, and Generative AI developments
What You Bring
Bachelor's or Master's in Computer Science, Data Science, AI, Mathematics, Engineering, or a related field
1–4 years of experience in ML, data science, or AI engineering
Strong Python skills
Solid grasp of ML algorithms and statistical concepts
Hands-on experience with Scikit-learn, Pandas, NumPy, and Matplotlib
Experience with supervised and unsupervised learning
Understanding of model evaluation, feature engineering, and hyperparameter tuning
Working knowledge of SQL and databases
Comfort with Git, plus sharp analytical and debugging skills
Bonus Points ✨
PyTorch or TensorFlow experience
Exposure to NLP, computer vision, recommendation systems, or Generative AI
Hands-on work with LLMs, embeddings, RAG, or vector databases
MLflow, Kubeflow, or Airflow
Docker and Kubernetes
AWS, Azure, or GCP
CI/CD and MLOps practices
Deploying models via REST APIs or microservices
Distributed computing with Spark
