Senior AI Engineer

Evren AI; United States; Remote; USD 190,000 - 260,000; Full time

Job summary

Evren AI is seeking a Senior AI Architect to define how intelligence is built into client products. You will lead architecture decisions, oversee production-grade AI systems, and mentor full-stack engineers on integration best practices.

In this senior role, you will shape model selection, deployment strategies, and cost-conscious, low-latency solutions while staying ahead of new AI capabilities and publishing insights.

Qualifications

• Five or more years of software engineering experience with at least two focused on production AI or ML systems.
• Deep experience with LLM integration including prompt engineering, retrieval-augmented generation, fine-tuning, and production-scale agents.
• Strong Python background and familiarity with LLM orchestration frameworks and vector databases.

Responsibilities

• Lead AI architecture and implementation across client product engagements, from model selection through production deployment.
• Design and build production LLM pipelines, including retrieval-augmented generation architectures, agent systems, and domain-specific fine-tuning workflows.
• Evaluate AI approaches model-agnostically, choosing the right tool based on product needs rather than hype.
• Contribute to the discovery phase by defining AI feasibility, data requirements, and integration strategy before a single sprint begins.
• Mentor full-stack engineers on AI integration best practices and architectural patterns.
• Track new models, frameworks, and techniques, and bring useful developments back to the team.
• Contribute to technical thought leadership through blog posts, internal playbooks, and open-source work.

Skills

LLM integration

Python

Production AI

System design

Technical writing

Tools

LangChain

Pinecone

Job description

Role overview

Define how intelligence gets built into every product a client engagement ships. This senior role goes well beyond proof-of-concept work and focuses on production-grade AI systems where latency, hallucination, cost, and context drift are real constraints. You will lead AI architecture decisions, set technical direction across client products, and mentor full-stack engineers on integration best practices.

Responsibilities

• Lead AI architecture and implementation across client product engagements, from model selection through production deployment.
• Design and build production LLM pipelines, including retrieval-augmented generation architectures, agent systems, and domain-specific fine-tuning workflows.
• Evaluate AI approaches model-agnostically, choosing the right tool based on product needs rather than hype.
• Contribute to the discovery phase by defining AI feasibility, data requirements, and integration strategy before a single sprint begins.
• Mentor full-stack engineers on AI integration best practices and architectural patterns.
• Track new models, frameworks, and techniques, and bring useful developments back to the team.
• Contribute to technical thought leadership through blog posts, internal playbooks, and open-source work.

Requirements

• Five or more years of software engineering experience, with at least two years focused on production AI or machine learning systems.
• Deep experience with LLM integration, including prompt engineering, retrieval-augmented generation, fine-tuning, and agent architectures at production scale.
• Strong Python background and familiarity with LLM orchestration frameworks and vector database ecosystems.
• Product-level judgment: a technically correct solution that ships late or confuses the user has failed.
• Clear written and verbal communication, with the ability to write a technical spec that a client's CTO can review and approve.
• Held opinions about AI architecture that can be defended and updated when better arguments appear.

Nice to have

• Computer vision pipelines.
• Predictive analytics and time-series modeling.
• Client-facing or consultancy experience.
• Published technical writing or open-source contributions.
• Data science background.