Senior Engineer Data Science

Colombo - Sri Lanka

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

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

Bachelors 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
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

Key ResponsibilitiesDesign 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 dataF...

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

Bachelors 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
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