Generative AI Engineer

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.