Gen AI Enterprise Architect-AVP
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 and launch enterprise AI systems using LLMs, retrieval-augmented generation, and agents across cloud platforms
- Shape the full GenAI architecture, from incoming data and model choices through orchestration, inference, integration, and user applications
- Choose and adapt commercial or open-source models for specific business needs, then set up versioning and CI/CD practices
- Connect AI systems to existing APIs and apply access controls, encryption, and PII masking to address security risks
- Guide clients through workshops and requirements gathering, turning business challenges into technical plans and governance frameworks
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 another listed field
- Strong client communication skills, with the ability to explain technical ideas to business audiences
- Hands-on expertise with Python, NLP, deep learning frameworks, orchestration tools, cloud AI services, and vector databases
Who this fits
This role suits an experienced AI or software engineer who can lead architecture decisions and advise clients directly. It is a full-time, hybrid role 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.