Responsibilities:
Integrates state-of-the-art machine learning algorithms as well as the development of new methods
Develops tools to support analysis and visualization of large datasets
Develops, codes software programs, implements industry standard auto ML models (Speech, Computer vision, Text Data, LLM), Statistical models, relevant ML models (devices/machine acquired data), AI models and algorithms
Identifies meaningful foresights based on predictive ML models from large data and metadata sources; interprets and communicates foresights, insights and findings from experiments to product managers, service managers, business partners and business managers
Makes use of Rapid Development Tools (Business Intelligence Tools, Graphics Libraries, Data modelling tools) to effectively communicate research findings using visual graphics, Data Models, machine learning model features, feature engineering / transformations to relevant stakeholders
Analyze, review and track trends and tools in Data Science, Machine Learning, Artificial Intelligence and IoT space
Interacts with Cross-Functional teams to identify questions and issues for data engineering, machine learning models feature engineering
Evaluates and makes recommendations to evolve data collection mechanism for Data capture to improve efficacy of machine learning models prediction
Meets with customers, partners, product managers and business leaders to present findings, predictions, foresights; Gather customer specific requirements of business problems/processes; Identify data collection constraints and alternatives for implementation of models
Working knowledge of MLOps, LLMs and Agentic AI/Workflows
Programming Skills: Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch
LLM Expertise: Hands-on experience in training, fine-tuning, and deploying LLMs
Foundational Model Knowledge: Strong understanding of open-weight LLM architectures, including training methodologies, fine-tuning techniques, hyperparameter optimization, and model distillation.
Data Pipeline Development: Strong understanding of data engineering concepts, feature engineering, and workflow automation using Airflow or Kubeflow.
Cloud & MLOps: Experience deploying ML models in cloud environments like AWS, GCP (Google Vertex AI), or Azure using Docker and Kubernetes.Designs and implementation predictive and optimisation models incorporating diverse data types
Strong in Pytho, Pyspark, SQl
Qualifications:
Bachelors degree, Masters or PhD in statistics, mathematics, computer science or related discipline preferred
0-2 years
Statistics modeling and algorithms
Machine Learning experience including deep learning and neural networks, genetics algorithm etc
Working knowledge with big data – Hadoop, Cassandra, Spark R. Hands on experience preferred
Data Mining
Data Visualization and visualization analysis tools including R
Work/project experience in sensors, IoT, mobile industry highly preferred
Excellent written and verbal communication
Comfortable presenting to Sr Management and CxO level executives
Self-motivated and self-starting with high degree of work ethic