Engineer - Data Science at Axiata Digital Labs

Colombo, Western Province, Sri Lanka -

Full Time

Start Date

Immediate

Expiry Date

07 Jul, 26

Salary

0.0

Posted On

08 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Deep Learning, TensorFlow, PyTorch, MLOps, LLMOps, Data Science, Feature Engineering, Cloud Platforms, Docker, Kubernetes, AIOps, Time-series Analysis, Vector Databases, CI/CD

Industry

IT Services and IT Consulting

Description

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 01+ 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 Exposure to API management platforms and security concepts (authentication, rate limiting, governance) Experience with CI/CD pipelines for ML and AI systems Prior experience in telecommunications, fintech, or large-scale enterprise environments Strong analytical and problem-solving skills with a pragmatic, engineering-first mindset Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders Comfortable working in cross-functional, agile teams Self-driven, accountable, and capable of owning solutions end-to-end Passion for continuous learning and applying emerging AI technologies responsibly Vendor submissions - 12 months

Responsibilities

Design, develop, and optimize machine learning and deep learning models while implementing MLOps and LLMOps pipelines. Collaborate with cross-functional teams to deploy scalable AI solutions and maintain model performance through rigorous monitoring and validation.