Senior Engineer (Data Science)
About the role
The Senior Engineer (Data Science) role involves designing, developing, and optimizing machine learning and deep learning models for a variety of data types and business use cases. The position is responsible for building and deploying models, implementing MLOps and LLMOps pipelines, developing monitoring solutions, and applying AIOps concepts for operational insights. The role also includes collaborating with platform teams to expose ML/AI capabilities via APIs, participating in data preparation, and mentoring junior team members. Candidates should have at least 3 years of hands-on experience in data science or machine learning engineering, strong Python and ML/DL library skills, and experience with MLOps tools, cloud platforms, and CI/CD pipelines. The position requires strong analytical skills, the ability to communicate complex concepts, and a passion for continuous learning.
Must have
- 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.
- 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.
Technologies
- Agile
- Anomaly Detection
- Authentication
- AWS
- Azure
- CI/CD
- Data Engineering
- Data Pipelines
- Data Science
- Deep Learning
- Docker
- Feature Engineering
- Fine-Tuning
- Fintech
- GCP
- Generative AI
- Kubeflow
- Kubernetes
- LLM
- LLMOps
- Machine Learning
- Microservices
- MLflow
- MLOps
- NumPy
- Observability
- pandas
- Prompt Engineering
- Python
- PyTorch
- RAG
- SageMaker
- scikit-learn
- TensorFlow
- Time Series
- Vector Databases
- Vertex AI
Highlights
Applications should be sent via email to gihan@mytechjobs.lk. Prior experience in telecommunications, fintech, or large-scale enterprise environments is mentioned as relevant. The role expects candidates to be comfortable working in cross-functional, agile teams.
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