1.Designing technology systems:Plan and design the structure of technology solutions, and work with design and development teams to assist with the process.
2.Communicating:Communicate system requirements to software development teams, and explain plans to developers and designers. They also communicate the
value of a solution to stakeholders and clients.
3.Managing Stakeholders:Work with clients and stakeholders to understand their vision for the systems. Should also manage stakeholder expectations.
4.Architectural Oversight:Develop and implement robust architectures for AI/ML and data science solutions, ensuring scalability, security, and performance. Oversee
architecture for data-driven web applications and data science projects, providing guidance on best practices in data processing, model deployment, and end-to-end
workflows.
5.Problem Solving:Identify and troubleshoot technical problems in existing or new systems. Assist with solving technical problems when they arise.
6.Ensuring Quality:Ensure if systems meet security and quality standards. Monitor systems to ensure they meet both user needs and business goals.
7.Project management:Break down project requirements into manageable pieces of work, and organise the workloads of technical teams.
8.Tool & Framework Expertise:Utilise relevant tools and technologies, including but not limited to LLMs, TensorFlow, PyTorch, Apache Spark, cloud platforms (AWS,
Azure, GCP), Web App development frameworks and DevOps practices.
9.Continuous Improvement:Stay current on emerging technologies and methods in AI, ML, data science, and web applications, bringing insights back to the team to foster continuous improvement.
Skills
1. Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn for developing machine learning and deep learning models.
2. Knowledge or experience working with self-hosted or managed LLMs.
3. Knowledge or experience with NLP tools and libraries (e.g., SpaCy, NLTK, Hugging
Face Transformers) and familiarity with Computer Vision frameworks like OpenCV and related libraries for image processing and object recognition.
4. Experience or knowledge in back-end frameworks (e.g., Django, Spring Boot, Node.js, Express etc.) and building RESTful and GraphQL APIs.
5. Familiarity with microservices, serverless, and event-driven architectures. Strong understanding of design patterns (e.g., Factory, Singleton, Observer) to ensure code scalability and reusability.
6. Proficiency in modern front-end frameworks such as React, Angular, or Vue.js, with an understanding of responsive design, UX/UI principles, and state management (e.g., Redux)
7. In-depth knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra), as well as caching solutions (e.g., Redis, Memcached).
8. Expertise in tools such as Apache Spark, Hadoop, Pandas, and Dask for large-scale data processing.
9. Understanding of data warehouses and ETL tools (e.g., Snowflake, BigQuery, Redshift, Airflow) to manage large datasets.
10. Familiarity with visualisation tools (e.g., Tableau, Power BI, Plotly) for building dashboards and conveying insights.
11. Knowledge of deploying models with TensorFlow Serving, Flask, FastAPI, or cloud-native services (e.g., AWS SageMaker, Google AI Platform).
12. Familiarity with MLOps tools and practices for versioning, monitoring, and scaling models (e.g., MLflow, Kubeflow, TFX).
13. Knowledge or experience in CI/CD, IaC and Cloud Native toolchains.
14. Understanding of security principles, including firewalls, VPC, IAM, and TLS/SSL for secure communication.
15. Knowledge of API Gateway, service mesh (e.g., Istio), and NGINX for API security, rate limiting, and traffic management.
Job Types: Full-time, Permanent
Pay: ?1,500,000.00 per year
Work Location: In person
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