Gen AI Enterprise Architect-AVP

Role: AVP, GenAI Enterprise Architect Experience Level: 8–15 Years | Location: US Remote Pay Range: $160K-$190K annual base + Incentives & Benefits Profile Focus: Hands-on AI Architecture, Pipeline Execution, & Client Delivery Design and deploy enterprise-grade AI solutions (LLMs, RAG, agents) by selecting appropriate models, building data pipelines, and integrating them with cloud platforms (AWS, Azure, GCP). Lead technical strategies across the standard 7-layer GenAI stack (from data ingestion to application interfaces), ensure scalability, manage AI security/hallucinations, and bridge business needs with engineering teams. ### Responsibilities - **System Design & Architecture:** Architect end-to-end Generative AI systems by operationalizing the 7-layer AI architecture (Data Sources, Preprocessing, Model Selection, Orchestration, Inference, Integration, and Application). - **Model Selection & Tuning:** Evaluate and select cutting-edge commercial (e.g., GPT-4) and open-source models, and fine-tune models for domain-specific use cases. - **LLMOps, Observability & Pipelines:** Establish LLMOps standards for model versioning and CI/CD. Implement foundational observability (OBS) layers using tools like Datadog, Splunk, or Prometheus to monitor system health, API latency, and basic application metrics. - **Integration & Data Protection:** Integrate AI solutions with existing APIs while enforcing core data protection measures, including Role-Based Access Control (RBAC), data encryption in transit, and basic PII (Personally Identifiable Information) masking to manage hallucinations and adversarial attacks. - **Strategic Leadership:** Collaborate with stakeholders to map business challenges to AI solutions and establish AI governance frameworks. - **Client Consulting:** Act as the primary onshore technical liaison, facilitating client workshops, requirements gathering, and translating business pain points into technical AI blueprints. ### Qualifications - **Consulting Skills:** Exceptional client-facing communication skills; proven ability to present complex technical concepts to business stakeholders. - **Technical Expertise:** Deep knowledge of NLP, Python, deep learning frameworks (PyTorch/TensorFlow), and orchestration tools (LangChain, Autogen). - **Cloud & Data Systems:** Extensive hands-on experience with AI services on AWS, Azure, or GCP. Expertise in vector databases (e.g., Pinecone, Milvus) and embedding techniques. - **Qualifications:** Bachelor’s / Master’s in Computer Science, AI, Data Science, or related field; 8–15 years in software engineering, ML, or AI roles, with demonstrable onshore consulting experience.