Generative AI Engineer

Company Description

Enterprise AI Solutions helps organizations adopt AI in ways that deliver measurable business results. The team brings enterprise-grade strategy, architecture, and execution to small and mid-sized businesses that are ready to put AI to work but lack in-house expertise. Services include AI strategy and roadmapping, implementation of AI agents, RAG systems, workflow automation, and rollout of enterprise AI tools with governance, privacy, security, and compliance built in. With 11+ years of experience in enterprise transformations and over 50 generative AI deployments, Enterprise AI Solutions focuses on practical outcomes that start delivering value from day one. The company partners closely with client teams to upskill their people and provide ongoing AI leadership and hands-on guidance.

Role Description

This is a full-time remote role for a Generative AI Engineer. The Generative AI Engineer will design, build, and optimize AI agents, RAG pipelines, and workflow automations on client data to solve real business problems. Day-to-day responsibilities include experimenting with LLM architectures and prompts, integrating AI capabilities into existing systems, and developing secure, scalable solutions using modern AI tools and cloud platforms. The role involves collaborating with consultants and client stakeholders to translate business requirements into technical designs, validating solutions through prototyping and testing, and documenting implementations for maintainability and knowledge transfer. The Generative AI Engineer will also contribute to internal best practices, tooling, and accelerators that improve delivery quality and speed across projects.

Qualifications

• Strong skills in Python and related AI/ML ecosystems, including experience with major LLM frameworks and APIs (e.g., Anthropic Claude, OpenAI, Google Gemini).
• Hands-on experience designing and implementing generative AI solutions such as RAG systems, AI agents, prompt engineering, and evaluation frameworks.
• Proficiency with cloud platforms and infrastructure for AI workloads (e.g., AWS, Azure, GCP), including secure data integration and workflow automation.
• Background in software engineering best practices, including testing, version control, CI/CD, and collaborative development in a team environment.
• Ability to work directly with business stakeholders to gather requirements, explain technical concepts clearly, and translate needs into practical AI solutions.
• Knowledge of data privacy, security, and compliance considerations when deploying AI in enterprise settings.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Prior experience delivering production-grade AI or ML solutions, especially in B2B or enterprise contexts, is highly beneficial.