Technical Architect

Key Responsibilities

1.

solutions, and work with design and development teams to assist with the process.

2.

teams, and explain plans to developers and designers. They also communicate the

value of a solution to stakeholders and clients.

3.

vision for the systems. Should also manage stakeholder expectations.

4.

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.

systems. Assist with solving technical problems when they arise.

6.

systems to ensure they meet both user needs and business goals.

7.

work, and organise the workloads of technical teams.

8.

not limited to LLMs, TensorFlow, PyTorch, Apache Spark, cloud platforms (AWS,

Azure, GCP), Web App development frameworks and DevOps practices.

9.

AI, ML, data science, and web applications, bringing insights back to the team to

foster continuous improvement.

Technical 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.