Senior Engineer

Brief Description

• Designing technology systems:Plan and design the structure of technology solutions, and work with design and development teams to assist with the process.
• 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.
• Managing Stakeholders:Work with clients and stakeholders to understand their vision for the systems. Should also manage stakeholder expectations.
• 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.
• Problem Solving: Identify and troubleshoot technical problems in existing or new systems. Assist with solving technical problems when they arise.
• Ensuring Quality:Ensure if systems meet security and quality standards. Monitor systems to ensure they meet both user needs and business goals.
• Project management:Break down project requirements into manageable pieces of work, and organise the workloads of technical teams.
• 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.
• 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.

Preferred Skills

• Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn for developing machine learning and deep learning models.
• Knowledge or experience working with self-hosted or managed LLMs.
• 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.
• Experience or knowledge in back-end frameworks (e.g., Django, Spring Boot, , Express etc.) and building RESTful and GraphQL APIs.
• Familiarity with microservices, serverless, and event-driven architectures. Strong understanding of design patterns (e.g., Factory, Singleton, Observer) to ensure code scalability and reusability.
• Proficiency in modern front-end frameworks such as React, Angular, or , with an understanding of responsive design, UX/UI principles, and state management (e.g., Redux)
• In-depth knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra), as well as caching solutions (e.g., Redis, Memcached).
• Expertise in tools such as Apache Spark, Hadoop, Pandas, and Dask for large-scale data processing. Understanding of data warehouses and ETL tools (e.g., Snowflake, BigQuery, Redshift, Airflow) to manage large datasets.
• Familiarity with visualisation tools (e.g., Tableau, Power BI, Plotly) for building dashboards and conveying insights.
• Knowledge of deploying models with TensorFlow Serving, Flask, FastAPI, or cloud-native services (e.g., AWS SageMaker, Google AI Platform).
• Familiarity with MLOps tools and practices for versioning, monitoring, and scaling models (e.g., MLflow, Kubeflow, TFX).
• Knowledge or experience in CI/CD, IaC and Cloud Native toolchains.
• Understanding of security principles, including firewalls, VPC, IAM, and TLS/SSL for secure communication.
• Knowledge of API Gateway, service mesh (e.g., Istio), and NGINX for API security, rate limiting, and traffic management.