Senior Engineer - Data Science

About the role

A Senior Data Science Engineer is sought to build and deploy production-grade machine learning, deep learning and generative AI solutions. The role covers building ML models, implementing MLOps and LLMOps pipelines, integrating AI into enterprise applications and delivering scalable, reliable, production-ready AI.

Key responsibilities

• Design, develop and optimise classical machine learning models (regression, classification, clustering, time-series forecasting, anomaly detection)
• Build and deploy deep learning models with TensorFlow or PyTorch for structured, unstructured and multimodal data
• Fine-tune and evaluate LLMs/SLMs for text classification, summarisation, information extraction and domain-specific reasoning
• Implement and maintain MLOps and LLMOps pipelines, including training, versioning, CI/CD, deployment, rollback and lifecycle management
• Develop model monitoring and observability covering performance, drift, bias, latency and cost
• Apply AIOps to automate detection, root cause analysis and predictive insights
• Work with API Manager and platform teams to expose ML/AI capabilities as secure, documented APIs
• Take part in data preparation, feature engineering and model validation and benchmarking
• Contribute to technical design documents, architecture reviews and best-practice guidelines
• Mentor junior engineers and interns, and stay current with ML, DL and generative AI advances

Required profile

• Bachelor's degree in IT, Computer Science, Data Science, Engineering, Mathematics or a related field
• 3+ years of hands-on experience in data science or machine learning engineering
• Proven experience deploying production-grade ML models
• Experience with APIs, microservices and integrating ML models into enterprise applications
• Prior experience in telecommunications, fintech or large-scale enterprise environments
• Strong analytical and problem-solving skills, clear communication and comfort in cross-functional agile teams

Required skills

• Python and ML/DL libraries (scikit-learn, PyTorch, TensorFlow, NumPy, pandas)
• MLOps platforms (MLflow, Kubeflow, SageMaker, Vertex AI or equivalent) and LLMOps (prompt engineering, fine-tuning, evaluation, serving)
• Cloud platforms (AWS, Azure, GCP) and Docker/Kubernetes
• CI/CD pipelines for ML and AI systems; API management and security concepts
• Time-series analysis, anomaly detection, telemetry data, vector databases, RAG pipelines and embedding models