AI Solution Architect - LLM & Generative AI (Remote)

Veltris; United States; Hybrid; USD 42,000 - 94,000; Full time

Benefits offered by this job

Health Insurance

Meal Card

Internet/Mobile Allowance

Elite Bonuses

Job summary

Veltris is seeking an accomplished AI Solution Architect to define and lead enterprise-grade AI platforms powered by large language models and agentic systems. You will partner with clients, product teams, and engineers to translate complex business problems into scalable, secure AI solutions.

You will define end-to-end architectures, engage stakeholders, evaluate models and tooling, and establish governance, security, and observability for production deployments across cloud environments.

Qualifications

• PhD or equivalent research doctorate in a technical field.
• 10+ years in software, data, or AI/ML with leadership exposure.
• 3+ years deploying LLM-based solutions in production.

Responsibilities

• Define end-to-end LLM-powered architectures (RAG, agentic workflows, multi-model orchestration).
• Engage with clients and stakeholders to translate business problems into scalable AI platforms.
• Lead evaluation, cost, latency, and risk decisions for models and tooling.
• Design guardrails, monitoring, and governance for production AI systems.
• Provide technical leadership and mentorship across teams.

Skills

LLM

Generative AI

RAG

Agentic AI

Prompt Engineering

Fine-tuning

Embeddings

Vector Databases

LangChain

LangGraph

LlamaIndex

GPT

Claude

Llama

Mistral

Gemini

Python

Solution Architecture

AI Architecture

MLOps

LLMOps

AWS

Azure

GCP

Cloud Native

Microservices

CI/CD

Responsible AI

Education

PhD in Computer Science / AI / ML

Job description

Veltris is seeking an accomplished AI Solution Architect to define and lead enterprise-grade AI platforms powered by large language models and agentic systems. You will partner with clients, product teams, and engineers to translate complex business problems into scalable, secure AI solutions.

You will define end-to-end architectures, engage stakeholders, evaluate models and tooling, and establish governance, security, and observability for production deployments across cloud environments.