You will own the end-to-end architecture and implementation of the AI assistant layer, including context augmentation pipelines and agentic systems. You are responsible for ensuring privacy, security, and performance optimization while mentoring team members and defining technical standards.
OUR VISION
EQUS is building the trust infrastructure for personal AI. Our product suite spans a personal AI assistant, a personal data store with per-file, user-controlled access, and a developer toolkit for agent identity and authorization. It all runs on a single permission rail designed around one principle: your data belongs to you. We are preparing for a major public launch and scaling from build mode to operate-at-scale mode.
WHAT YOU WILL DO
As a Senior AI Engineer at EQUS, you own the AI assistant layer end to end: architecture, standards, and the service contracts that security, storage and front-end engineers support. You are a technical decision-maker, not only an implementer. You recommend the right approach for each problem, and you are prepared to say "this does not need AI" when it doesn’t. Privacy, safety, and cost sit at the center of every call you make. Your day-to-day includes, but is not limited to:
AI architecture and context engineering. Design the context augmentation pipelines, spanning vector RAG, CAG, agentic file exploration, Text-to-SQL, knowledge graphs, fine-tuning, and MCP-based context engineering, and select the right approach for each use case. Select chunking strategy, embedding models, and retrieval architecture for user-owned document systems with multi-tenant isolation. Privacy and security guides each decision.
LLM integration and agent systems. Integrate and manage commercial and open-source LLM APIs, and deploy multi-agent systems with LangChain, LlamaIndex, or LangGraph. Lead model selection, prompt engineering, fine-tuning, and production evaluation across the AI stack.
Evaluation and optimization. Build evaluation frameworks that measure output quality, relevance, and safety. Optimize pipelines for latency, token cost, and throughput, monitor production for drift and regression, and close the feedback loop from evals back into iteration.
Privacy and safety engineering. Privacy is our product, not a compliance checkbox. Own PII handling, GDPR and CCPA compliance, encryption at rest and in transit, and user-scoped access boundaries at the systems level. Build prompt injection defenses, output filtering, and data leakage prevention, and partner with security and trust experts on agentic workflow guardrails and shadow AI detection.
Infrastructure and standards. Deploy and operate production AI systems on AWS, Docker and Kubernetes, and GitLab. Define the AI service contracts and APIs other engineers build on top of, and set the standard for how AI works here, including mentoring engineers and raising the technical bar around you.
WHAT YOU WILL NEED TO SUCCEED
You have shipped AI in production, you know where pipelines break, and you make architectural decisions that prove right. You exercise strong judgment on build vs. buy and on what separates an MVP from a production system, and you know the cost-performance tradeoffs cold: when a smaller fine-tuned model outperforms a general-purpose large one, and when expanding the context window beats RAG.
You treat AI safety as a first-class engineering concern rather than a review-stage checklist. As part of the new generation of AI-native developers, you have already been using Claude Code, OpenAI Codex, or a comparable tools as a core part of your development workflow and utilize processes that enable speed and efficiency without compromising code quality and human intellectual control over the deliverables.
You have experience in both small and large teams, effectively use tools such as Jira and Confluence, and are a valuable colleague to product managers as new features and products are emerging. You have experience working effectively with consultants and outsourced development teams, including transitioning responsibilities for systems.
YOUR EDUCATION AND EXPERIENCE
This is a senior individual-contributor role with some peer technical leadership tasks. A relevant degree in computer science or engineering is preferred but highly qualified individuals with demonstrated experience shipping AI products at scale are welcome. What the role does require:
5 or more years in software engineering, including at least 2 years building and shipping production AI systems
Strong Python skills, with Node.js or .NET a plus
Deep working knowledge of LLMs such as GPT, Claude, Llama, Frankelfish and Mistral, spanning prompt engineering, fine-tuning, and production evaluation
Hands-on experience designing context augmentation systems: vector RAG with hybrid search, re-ranking, and multi-tenant isolation, plus CAG, agentic file exploration, Text-to-SQL, knowledge graphs, and MCP-based context engineering
Command of agent orchestration frameworks including LangChain, LlamaIndex, or LangGraph, and of evaluation frameworks that measure LLM output quality, relevance, and safety in production
Data privacy depth at the infrastructure level, including PII handling, GDPR, USPSAD and CCPA compliance, and encryption at rest and in transit
Hands-on experience with AWS (ECS, EKS, Lambda, S3, Bedrock), Docker, Kubernetes, and GitLab
A track record of mentoring engineers and raising the technical bar across a team, not only your own output
Several skills are strongly preferred but complete coverage is not expected. Experience running local open-source models such as Llama, Mistral, or Mixtral via Ollama, vLLM, or llama.cpp is a significant plus, as is fine-tuning with LoRA or QLoRA. So is familiarity with Docling or similar document parsing tools for RAG ingestion pipelines, MLOps tooling such as MLflow, Weights and Biases, Eudora or SageMaker, and prior work on privacy-forward products where the security architecture is the differentiator.
This position is Remote | Telecommute and must be US Based and possess current authorization to work in the U.S. without sponsorship.
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