GenAI/ML Engineer | Generative AI (GenAI) Solutions Architect

Key Responsibilities

• 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.

Required Skills & Qualifications

• 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.

Nice-to-Have

• 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.