Responsibilities
Key Responsibilities:
• Research & Innovation:
o Stay up-to-date with the latest advancements in deep learning, LLMs, and statistical methods.
o Conduct experiments to explore new techniques and improve existing models.
o Perform applied research and experimentation, validate model performance for accuracy, robustness & champion responsible AI practices
• Deep Learning & LLM Development:
o Design, implement, and optimize deep learning models and frameworks (e.g., TensorFlow, PyTorch, JAX).
o Develop and fine-tune large language models (LLMs) for specific use cases, including natural language processing (NLP) tasks.
o Experiment with state-of-the-art architectures (e.g., Transformers, GPT, BERT) to solve complex problems.
o Knowledge of Agentic frameworks (Crew AI , llama index etc).
o Good knowledge of GenAI framework
• Statistical Modelling & Analysis:
o Apply advanced statistical methods to analyse data, validate models, and interpret results.
o Develop probabilistic models and leverage Bayesian inference techniques where applicable.
o Ensure models are statistically robust and generalize well to real-world data.
• Data Preprocessing & Feature Engineering:
o Clean, preprocess, and transform large datasets for training and evaluation.
o Perform feature engineering and dimensionality reduction to improve model performance.
• Model Deployment & Optimization:
o Deploy deep learning models and LLMs into production environments.
o Optimize models for inference speed, memory usage, and scalability.
o Implement monitoring and evaluation systems to track model performance over time.
• Collaboration & Leadership:
o Work closely with cross-functional teams, including data scientists, engineers, and product managers.
o Mentor junior team members and provide technical guidance.
Profile required
Requirements and skills
• 7+ years in applying AI/ML principles to real world applications
• Broad NLP knowledge: tokenisation, part-of-speech tagging, dependency parsing, syntactic parsing, word sense disambiguation, topic modeling; contextual text mining, Word embedding
• Experience Computer Vision:
o Construction, Feature detection, Segmentation, Classification
o object detection, tracking, localisation, classification, recognition, scene understanding
• Experience to Deep Learning - CNNs, LSTMs, network architecture, network tuning, transfer learning, multi-task learning
• Machine Learning experience - Algorithm Evaluation, Preparation, Analysis, Modeling and Execution.
• Experience to Open source NLP libraries e.g. NLTK, Regex, Stanford NLP, OpenNLP/CoreNLP
• Very strong grasp of IT concepts with a strong algorithms/data structures background
• Demonstrated history of building prototypes to win business confidence.
• Experience in using Keras, Tensorflow, Caffe and/or other neural network development frameworks.
• Experience with common data science toolkits, such as Scikit, NumPy, R libraries - Excellence in at least one of these is mandatory.
• Proficiency with any one NoSQL databases such as MongoDB, Cassandra, HBase.
• Should have prior experience in developing APIs, services using either C# or Java.
• Experience in Develop, implement, and optimize machine learning models using the Microsoft AI Platform (Azure Machine Learning, Cognitive Services, etc.)
• Excellent understanding of machine learning techniques and algorithms, such as SVM, Decision Forests, k-NN, Naive Bayes etc.
• Experience in selecting features, building and optimizing classifiers using machine learning techniques.
• Should have good awareness on entire machine learning/ predictive modeling & implementation.
Additional Qualifications:
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Knowledge of natural language processing (NLP) and computer vision techniques.
• Familiarity with DevOps practices and CI/CD pipelines for machine learning models.
• Microsoft Azure certifications (e.g., Azure Data Scientist Associate, Azure AI Engineer Associate).
• Research business domain and develop use-cases to support enterprise wide AI solutions.
• Monitor and maintain deployed models, ensuring scalability, performance, and accuracy.
• Prior experience with data visualization tools, such as D3.js, GGplot, etc..
• Good knowledge on statistics skills, such as distributions, statistical testing, regression, etc..
• Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders.
• Basic understanding of the banking industry is value add.