Gen AI Architect

The GenAI Architect is responsible for designing, guiding, and implementing enterprise‑grade Generative AI solutions embedded within product platforms. This role bridges AI research, engineering, and product development, ensuring GenAI capabilities are scalable, secure, and aligned with business objectives.

• Design and own the end‑to‑end architecture for GenAI‑powered product features.
• Guide engineering teams on best practices for GenAI development and integration.
• Establish standards and patterns for prompts, agents, and inference layers.
• Ensure security, compliance, and responsible AI principles are built into every solution.

• 8+ years of experience in software architecture, ML engineering, or platform engineering.
• 2+ years of hands‑on experience with AI/ML systems, including Generative AI.
• Strong software engineering background (Python, Java, or similar).
• Prior work with enterprise AI governance or regulated industries.
• Familiarity with open‑source AI ecosystems.
• Background in data platforms or analytics engineering.

• Strong understanding of Generative AI models (LLMs, multimodal models, embeddings).
• Hands‑on experience with foundation models (e.g., GPT‑style, Claude‑style, LLaMA‑style) and model adaptation techniques.
• Expertise in prompt engineering, prompt orchestration, and agent‑based frameworks.
• Solid grounding in machine learning fundamentals, including supervised/unsupervised learning, evaluation metrics, and inference optimization.
• Ability to design scalable, modular GenAI architectures for production use.
• Experience with:
  • RAG (Retrieval‑Augmented Generation) architectures
  • Vector databases (semantic search, embeddings indexing)
  • Multi‑agent systems and workflow orchestration
• Strong understanding of low‑latency inference, model routing, and fallback strategies.
• Knowledge of event‑driven, microservices, and API‑first architectures.
• Experience integrating GenAI capabilities into customer‑facing and internal products.
• Ability to translate product requirements into AI‑driven capabilities and technical designs.
• Familiarity with A/B testing, feature flags, and iterative product releases involving AI.
• Proficiency in data pipelines, feature engineering, and unstructured data processing.
• Experience with:
  • Knowledge graphs
  • Metadata‑driven architectures
  • Document ingestion and chunking strategies
• Strong understanding of data quality, provenance, and governance for AI systems.

• Strong experience in cloud‑native environments (GCP).
• Familiarity with MLOps practices, including:
  • Monitoring, logging, and drift detection
• Experience with containerization, Kubernetes, and CI/CD pipelines.
• Knowledge of inference optimization and cost‑control strategies.

• Understanding of AI security risks (prompt injection, data leakage, model abuse).
• Experience implementing guardrails, content filters, and policy enforcement.
• Knowledge of responsible AI practices, including explainability, bias mitigation, and compliance.
• Familiarity with data privacy regulations (e.g., GDPR, enterprise governance standards).