Gen AI Engineer - Contract / Freelance

Gen AI Engineer - Contract / FreelanceRole OverviewAs 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. And of course, all this happens in a consultative, collaborative fashion – in keeping with our values.Key Responsibilities: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 ongoing 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.Required Skills:Technical Proficiency: Advanced experience in Python.Cloud Expertise: Familiarity with Azure or AWS Bedrock is advantageous.RAG & Vector Databases: Hands-on experience with RAG and working with vector databases.Prompt & Model Optimization: Knowledge of prompt engineering, model evaluation techniques, and monitoring tools.Software Development Methodologies: Proficiency in CI/CD, load testing, and test-driven development for AI/ML applications.Ideal Experience:Individuals from both a data science and data engineering background often excel in this role, given their ability to apply both experimental and engineering mindsets. Ideal candidates:Have an evaluation mindset and understand the non-deterministic nature of GenAI systems.Are committed to continuous improvement and quality standards in solutions.Avoid the “magic box” approach, instead setting clear expectations on performance and accuracy.ApplicationPlease submit your resume and cover letter detailing your experience with GenAI technologies, including any relevant examples of prior work.