Bullfinch Recruitment is hiringSenior Generative AI Engineer

We’re using generative AI to reimagine how data turns into action. As a Senior Generative AI Engineer, you’ll be a key technical leader on the AI team—driving innovation, leading complex build efforts, and helping shape the future of how AI powers our platforms.

This is a hands-on role for someone who’s both a deep technologist and a strategic thinker—ready to own high-impact projects from design through deployment. You’ll work closely with product, engineering, and data teams to ensure that generative AI capabilities are scalable, ethical, and aligned to real-world needs.

We’re looking for someone who thrives at the edge of research and implementation, loves solving tough problems, and sees mentorship as part of their craft.

What You’ll Be Doing

• Design, build, and deploy advanced generative AI systems—including LLMs, diffusion models, and other architectures—for applications like data synthesis, intelligent content generation, and predictive analytics.

• Take technical ownership of key initiatives, from early prototyping to production deployment—collaborating across disciplines and ensuring high performance, reliability, and security.

• Drive innovation in prompt design, fine-tuning strategies, and RAG implementations to ensure model outputs are contextual, trustworthy, and actionable.

• Develop and maintain robust APIs and microservices that connect AI models to the broader data and product ecosystem.

• Provide guidance, code reviews, and best practices to help elevate the broader team’s technical capability and understanding of generative AI.

• Apply and advocate for ethical principles, data governance, and model transparency in everything you build—aligned with best-practice governance standards.

• Work closely with product managers, platform engineers, and data scientists to align technical solutions with business goals.

What You’ll Bring

Required Skills

• 4–6 years of experience in AI/ML engineering, including hands-on work with generative models and AI infrastructure.

• Proven experience deploying LLMs, diffusion models, or other generative AI technologies using frameworks like Hugging Face, PyTorch, or TensorFlow.

• Strong Python programming skills and familiarity with modern MLOps workflows, CI/CD, and version control.

• Expertise in prompt engineering, fine-tuning, and RAG techniques.

• Experience building scalable APIs and deploying AI services on cloud platforms—particularly Google Cloud (Vertex AI, Generative AI Studio, etc.).

• Strong communication and collaboration skills, with the ability to translate technical ideas into clear, actionable outcomes.

Nice-to-Have Skills

• Google Cloud ML or Data Engineering certifications.

• Experience mentoring engineers or leading small cross-functional pods.

• Familiarity with orchestration tools (e.g., Kubeflow, Airflow) and model monitoring frameworks.

• Contributions to open-source AI projects or involvement in AI research communities.