GenAI​/ML Engineer | Generative AI; GenAI Solutions Architect

About the Role

We are seeking an experienced Generative AI Solutions Architect to lead the design and implementation of cutting‑edge GenAI solutions. You will define the architecture, lead development efforts, and ensure scalable, ethical deployment of AI systems—from model selection to production. This role requires deep technical expertise in RAG, vector stores, prompt engineering, and ML deployment
, along with strong leadership to guide teams and stakeholders.

• Define end‑to‑end GenAI architecture
  , including model selection, fine‑tuning, retrieval‑augmented generation (RAG), vector databases, and prompt engineering pipelines.
• Design and deploy scalable software applications to support Generative AI initiatives.
• Build Minimum Viable Products (MVPs) for rapid iteration in dynamic environments.

• Hands‑on model deployment from development to production, with troubleshooting and optimization.
• Collaborate with Data Scientists, MLOps, and Cloud Architects to ensure robust, compliant AI systems.

• Lead a small squad of engineers
  , providing technical guidance and fostering a high‑performance culture.
• Mentor engineers of all levels and drive best practices in AI/ML development.
• Partner with Product, Legal, and Leadership to align AI solutions with ethical, regulatory, and business goals.

• Proactively resolve complex technical challenges across the AI/ML stack.
• Translate technical concepts for executives, engineers, and cross‑functional teams
  .

• Proven experience in GenAI architecture (RAG, vector stores, prompt engineering).
• Hands‑on ML engineering skills: model training, deployment, and production troubleshooting.
• Expertise in Python and modern software development practices.
• Track record of delivering MVPs and scalable AI solutions.
• Strong leadership: ability to mentor engineers and lead technical teams.

• Familiarity with LLM fine‑tuning (e.g., GPT, Llama, Claude).
• Experience with cloud platforms (AWS/Azure/GCP) and MLOps tools.
• Knowledge of AI ethics, compliance, and governance
  .