Senior Technical Lead

About this role

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Skills you'll use

What you'll do

• Design, build, test, and deploy robust and scalable AI/ML models and applications. Take ownership of features from initial concept through to production and ongoing maintenance.
• -Contribute to the technical and architectural design of new AI systems and services, ensuring they meet standards for performance, security, and scalability.
• -Implement state-of-the-art machine learning and deep learning models. Fine-tune and optimize models for specific business use cases, focusing on accuracy and efficiency.
• -Analyze complex business requirements and translate them into well-architected, practical, and effective AI solutions.
• -Uphold high standards for code quality, testing, and documentation. Champion software engineering best practices within the team. Deliver code that is secure, reliable and supportable.
• -Provide technical guidance and mentorship to junior engineers, assisting with code reviews and sharing knowledge to elevate the team's overall capabilities.
• -Demonstrate a strong curiosity for leveraging AI to improve personal and team productivity. Actively find and implement AI-powered tools and workflows to make your own role and development processes more efficient. Effectively use AI code assistants to deliver code.
• -Ensure application diagrams and documentation stay current and relevant.
• -Adherence to agile development methodologies.

Who can apply

• Deep understanding of modern AI concepts, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows.
• -Proficiency in Python and extensive experience with its scientific computing and ML/DL libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
• -Strong theoretical and practical understanding of machine learning, deep learning, and natural language processing (NLP).
• -Hands-on experience with at least one major cloud provider (GCP, AWS, Azure) and their associated AI/ML services.
• -Familiarity with MLOps principles and tools for model versioning, deployment, and monitoring (e.g., Docker, Kubernetes, MLflow).
• -Experience with both SQL and NoSQL databases, and proficiency with data processing technologies like Spark is a plus.
• -Solid understanding of microservices architecture, API design (e.g., REST, gRPC), and containerization technologies (e.g., Docker, Kubernetes).
• -Strong analytical and problem-solving skills
• -Ability to display effective verbal and written communication skills when explaining complex technical issues to a variety of technical audiences, including clients, vendors, senior management and staff.
• -Direct experience with a major generative AI platform (e.g., Google Gemini, OpenAI).

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