Lead Gen-AI Engineer

Equal Experts is an innovative consultancy specialising in the delivery of custom software solutions for blue-chip enterprise and public sector clients across a range of industry sectors. We work on important projects for our clients, deliver market-leading propositions across the digital, and mobile channels, and are recognised for our leadership in the application of agile and lean delivery methods to assure delivery. We have offices in Australia, India, South Africa, the UK, the EU and the USA.

This role involves working in teams that use modern agile technical practices - including continuous integration and deployment and fast feedback loops - to deliver timely and pragmatic solutions, as well as helping others to do their jobs in a more effective way.

Responsibilities

• As a Gen AI Engineer, you will be responsible for designing, testing, and deploying GenAI solutions that leverage LLMs and related technologies. You’ll work on solutions that incorporate prompt engineering, model evaluation, retrieval-augmented generation (RAG), and continuous performance monitoring, all while adhering to best practices in software engineering. This role blends data science, data engineering, and MLOps expertise, with a focus on making AI solutions reliable and production-ready.

Required Skills

• Solution Prototyping & Testing: Rapidly test various models and approaches to determine the best fit for the project without extensive over-investment. Stay updated on LLM advancements (e.g., GPT-3.5 to GPT-4).
• Prompt Engineering: Write, test, and refine prompts, including creating effective prompt chains and managing prompt versions for optimal results.
• Model & Prompt Evaluation: Analyze model and prompt performance to ensure reliability. Develop robust testing methodologies, incorporating training and test sets for thorough evaluation.
• Performance Monitoring: Build pipelines to monitor model responses and track performance metrics in production for optimization.
• RAG Architecture Implementation: Design and maintain retrieval-augmented generation (RAG) architectures. Work with data pipelines, vector databases, and search algorithms. Manage document chunking and embedding selection for optimal context window usage.
• Data Extraction: Extract data from various sources (e.g., PDF files) as part of the RAG process.
• Software Development Best Practices: Employ CI/CD, test-driven development, and sound architectural principles in all GenAI solutions.
• Evaluation Scripts & Load Testing: Develop tests for the random nature of LLM outputs and conduct load testing to ensure system robustness, even when reliant on third-party APIs.
• ETL & Traditional Data Management: Build data pipelines to manage and integrate traditional relational data where necessary.

• As a Gen AI Engineer, you will be responsible for designing, testing, and deploying GenAI solutions that leverage LLMs and related technologies. You’ll work on solutions that incorporate prompt engineering, model evaluation, retrieval-augmented generation (RAG), and continuous performance monitoring, all while adhering to best practices in software engineering. This role blends data science, data engineering, and MLOps expertise, with a focus on making AI solutions reliable and production-ready.
• Solution Prototyping & Testing: Rapidly test various models and approaches to determine the best fit for the project without extensive over-investment. Stay updated on LLM advancements (e.g., GPT-3.5 to GPT-4).
• Prompt Engineering: Write, test, and refine prompts, including creating effective prompt chains and managing prompt versions for optimal results.
• Model & Prompt Evaluation: Analyze model and prompt performance to ensure reliability. Develop robust testing methodologies, incorporating training and test sets for thorough evaluation.
• Performance Monitoring: Build pipelines to monitor model responses and track performance metrics in production for optimization.
• RAG Architecture Implementation: Design and maintain retrieval-augmented generation (RAG) architectures. Work with data pipelines, vector databases, and search algorithms. Manage document chunking and embedding selection for optimal context window usage.
• Data Extraction: Extract data from various sources (e.g., PDF files) as part of the RAG process.
• Software Development Best Practices: Employ CI/CD, test-driven development, and sound architectural principles in all GenAI solutions.
• Evaluation Scripts & Load Testing: Develop tests for the random nature of LLM outputs and conduct load testing to ensure system robustness, even when reliant on third-party APIs.
• ETL & Traditional Data Management: Build data pipelines to manage and integrate traditional relational data where necessary.