SAVP, GenAI Enterprise Architect
At a glance
Mid-level AI Engineering role at Fa Ewjt Saasfaprod1. United States · full-time · no salary stated · we estimate $132,450–$205,151 across the United States.
Fa Ewjt Saasfaprod1 has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Design end-to-end GenAI systems, from data sources and model choice through orchestration, inference and user-facing applications
- Select and tune commercial or open-source models for specific business needs, then build the pipelines that support them
- Set LLMOps practices for model versions and CI/CD, and monitor system health, API latency and application metrics
- Connect AI systems to existing APIs while applying access controls, encryption and basic PII masking
- Lead client workshops, turn business problems into technical AI plans, and shape AI governance with stakeholders
What you bring
- Eight to fifteen years in software engineering, machine learning or AI, including onshore consulting work
- A bachelor’s or master’s degree in computer science, AI, data science or a related area
- Strong NLP, Python, deep learning and orchestration experience, plus hands-on work with AWS, Azure or GCP AI services
- Experience with vector databases and embeddings, and the ability to explain technical ideas to business audiences
Who this fits
This role suits a senior technical consultant who can shape AI architecture and work directly with clients as their primary onshore technical contact. The position is hybrid in the United States.
From the employer
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.
- 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.
- 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.