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
