Design, build, and scale machine learning systems, including recommendation engines and predictive models for real-world business applications
Analyze both structured and unstructured data, applying advanced ML and NLP techniques to extract insights and power intelligent products
Own the end-to-end model lifecycle: from data preprocessing, experimentation, model training, evaluation, and tuning to deployment and continuous monitoring in production environments
Apply MLOps best practices, ensuring reproducibility, versioning, CI/CD integration, and performance monitoring
Translate complex business problems into robust ML solutions in collaboration with product, engineering, and domain experts
Communicate clearly and effectively with both technical and non-technical stakeholders, articulating model behavior, assumptions, and trade-offs
Continuously evaluate and implement state-of-the-art techniques, staying current with developments in ML, NLP, and deep learning
Contribute to team growth, sharing expertise and mentoring peers in modeling, tooling, and system design
requirements-expected :
At least 4 years of hands-on experience in machine learning, data science, or a similar role, with demonstrated ownership of deployed ML systems
Strong grasp of core ML techniques: regression, classification, clustering, regularization, ensemble methods, neural networks, etc.
Solid experience with Natural Language Processing (NLP) from traditional methods (TF-IDF, topic modeling) to transformer-based architectures (e.g., BERT, GPT, T5)
Working knowledge of recommendation engines, such as collaborative filtering, content-based filtering, or hybrid models
Expertise in Python and ML/data libraries such as scikit-learn, xgboost, lightgbm, pandas, numpy, PyTorch or TensorFlow/Keras
Experience with at least one major cloud platform (AWS, Azure, GCP), including usage of managed ML services or containerized deployment workflows
Understanding of MLOps principles, including reproducibility, model monitoring, A/B testing, and automation
Strong communication and storytelling skills, able to align model design with business value
A collaborative mindset and the ability to work effectively within cross-functional teams
Technologies - Python, Machine Learning (Supervised/Unsupervised Learning, Deep Learning), NLP (traditional and transformer-based), PyTorch / TensorFlow / Keras, Cloud platforms (AWS, Azure, or GCP), Git, CI/CD tools
Good English communication and written skill- English will be the working language
offered :
You influence the tools, technologies, and modeling approaches used
You contribute to product direction by shaping how ML is embedded into our offerings
You have autonomy in experimentation, model design, and implementation
We value learning, iteration, and invest in your growth as an ML expert
