Mactores is a trusted leader among businesses in providing modern data platform solutions. Since 2008, Mactores have been enabling businesses to accelerate their value through automation by providing End-to-End Data Solutions that are automated, agile, and secure. We collaborate with customers to strategize, navigate, and accelerate an ideal path forward with a digital transformation via assessments, migration, or modernization.
We are seeking a highly skilled and innovative Generative AI Engineer to join our team. In this role, you will develop and deploy cutting-edge generative AI models to solve real-world problems. You will work on building models that generate content, understand complex data, and collaborate closely with cross-functional teams to implement AI-powered solutions.
What you will do?
Design and implement generative AI solutions using large language models (LLMs).
Apply prompt engineering techniques and build scalable Retrieval-Augmented Generation (RAG) systems.
Fine-tune and optimize models for performance, cost, and reliability.
Leverage AWS services such as Bedrock, SageMaker, and Lambda for deployment and inference.
Develop APIs and backend components for production-grade AI applications.
Implement observability, performance monitoring, and security best practices.
Drive responsible AI adoption through evaluation, bias detection, and compliance.
What are we looking for?
3+ years of experience in Python with strong software engineering fundamentals.
Hands-on experience with LLMs and prompt engineering strategies.
Experience designing RAG pipelines and working with vector databases.
Proficiency in model fine-tuning (e.g., LoRA) and embedding-based systems.
Experience with cloud platforms and deploying AI models in production.
Strong debugging, optimization, and problem-solving skills.
Clear and effective technical communication.
Production-first mindset with attention to cost, reliability, and performance.
Preferred Qualifications
Practical experience with frameworks like LangChain or LlamaIndex.
Exposure to multi-modal AI systems.
Familiarity with ML/MLOps and large-scale deployment practices.
Experience supporting systems at high request volumes.