Responsibilities:
- Collaborate with data scientists, product managers, and business stakeholders to understand business needs and translate them into technical requirements.
- Design and implement machine learning models using a variety of algorithms, including (but not limited to) Predictive and Prescriptive ML Modelling, General Machine Learning, NLP/NLU :
- Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Random Forests, Gradient Boosting, XGBoost, Neural Networks (Deep Learning)
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis (PCA), Hierarchical Clustering
- NLP Techniques: Named Entity Recognition (NER), Text Classification, Sentiment Analysis, Topic Modeling, Machine Translation
- Perform exploratory data analysis (EDA) techniques to understand data characteristics, identify patterns, and prepare data for modeling. These techniques may include:
- Data Visualization: Scatter plots, Histograms, Boxplots, Heatmaps
- Statistical Analysis: Descriptive statistics, Hypothesis testing, Correlation analysis
- Select and implement appropriate statistical modeling techniques, such as time series analysis, survival analysis, and Bayesian statistics.
- Develop and implement mathematical models to represent real-world problems.
- Train and evaluate machine learning models, fine-tune hyperparameters, and monitor model performance in production.
- Deploy machine learning models on-premise, in the cloud (AWS, Azure, GCP), and at the edge, ensuring scalability, efficiency, and maintainability.
- Perform exploratory data analysis to understand the characteristics and relationships within datasets.
- Collaborate with DevOps and IT teams to integrate machine learning solutions into existing systems and workflows.
- Automate Machine Learning Pipelines for Efficient Model Development And Deployment.
- Continuously learn and stay up-to-date with the latest advancements in Machine Learning, NLP, And Related Fields.
- Document technical designs, methodologies, and results effectively.
- Collaborate with cross-functional teams to integrate machine learning solutions into existing systems.
Machine Learning Algorithms (Experience with some or all is a plus):
- Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Random Forests, Gradient Boosting, XGBoost
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GANs)
- LLM modelling Experience
Qualifications:
- 7-9 years of experience in machine learning engineering or a related field Predictive and Prescriptive ML model/General Machine Learning / NLP.
- Bachelor’s or Master's Engineering degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
- Strong understanding of Machine Learning Algorithms, Statistical Modelling Techniques, and EDA.
- Proven experience in designing, developing, and deploying machine learning models in production.
- Experience with cloud platforms (AWS, Azure, GCP) and/or on-premise deployments is a plus.
- Experience with edge computing is a plus.
- Excellent programming skills in Python (Scikit-learn, TensorFlow, PyTorch etc.).
- Experience with data wrangling and manipulation libraries (Pandas, NumPy).
- Strong communication, collaboration, and problem-solving skills.
- Familiarity with MLOps practices and tools
Solid foundation in Explainable AI (XAI) concepts
