AVP, 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 GenAI systems across data, model, orchestration, inference, integration and application layers
- Choose and tune commercial or open-source models, then build pipelines and connect AI systems to cloud platforms
- Set standards for model versioning and CI/CD, and monitor system health, API latency and application metrics
- Protect connected systems with access controls, encryption and PII masking while addressing hallucinations and adversarial attacks
- Lead client workshops to turn business challenges into technical AI plans and governance frameworks
What you bring
- Eight to fifteen years in software engineering, machine learning or AI roles, including onshore consulting
- A bachelor’s or master’s degree in computer science, AI, data science or a related field
- Hands-on expertise with Python, NLP, deep learning frameworks and orchestration tools such as LangChain or Autogen
- Experience with AI services on AWS, Azure or GCP, plus vector databases and embedding methods
- Strong client communication skills, including explaining technical concepts to business stakeholders
Who this fits
This role suits a senior technical architect who can work directly with clients and translate business needs into AI designs. It includes an onshore client liaison role and is listed as 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.