Senior Engineer - Data Science

Key Responsibilities

• Design, develop, and optimize classical machine learning models (e.g., regression, classification, clustering, time-series forecasting, anomaly detection)
• Build and deploy deep learning models using frameworks such as TensorFlow or PyTorch for structured, unstructured, and multimodal data
• Fine-tune and evaluate language models (LLMs/SLMs) for tasks such as text classification, summarization, information extraction, and domain-specific reasoning
• Implement and maintain MLOps and LLMOps pipelines, including model training, versioning, CI/CD, deployment, rollback, and lifecycle management
• Develop model monitoring and observability solutions covering performance, drift detection, bias, latency, and cost metrics
• Apply AIOps concepts to automate detection, root cause analysis, and predictive insights using operational and telemetry data
• Collaborate with API Manager and platform teams to expose ML/AI capabilities as secure, scalable, and well-documented APIs
• Participate in data preparation and feature engineering, working closely with data engineering teams and feature stores
• Perform rigorous model validation, experimentation, and benchmarking, ensuring reliability and reproducibility
• Contribute to technical design documents, architecture reviews, and best-practice guidelines
• Mentor junior engineers/interns and contribute to raising overall data science and engineering standards within the team
• Stay up to date with advancements in machine learning, deep learning, and generative AI, and assess their applicability to business use cases

Person Specifications

• Bachelor's degree in IT/Computer Science, Data Science, Engineering, Mathematics, or a related field
• 03+ years of hands-on experience in data science or machine learning engineering roles
• Strong experience with Python and common ML/DL libraries (scikit-learn, PyTorch, TensorFlow, NumPy, pandas)
• Proven experience developing and deploying production-grade ML models
• Hands-on experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent)
• Practical exposure to LLMOps, including prompt engineering, fine-tuning, evaluation, and model serving
• Experience working with APIs, microservices, and integrating ML models into enterprise applications
• Solid understanding of data pipelines, feature engineering, and model lifecycle management
• Experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)
• Experience applying AIOps techniques in monitoring, observability, or IT/network operations contexts
• Knowledge of time-series analysis, anomaly detection, or large-scale telemetry data
• Familiarity with vector databases, RAG pipelines, and embedding models