Senior AI Engineer / Architect

Location:
Fully Remote

Team:
AI & Engineering Leadership

Employment Type:
Contract / Contract-to-Hire

About the Role

We design and build production-grade AI systems for a range of clients, including generative media pipelines, retrieval-augmented knowledge systems, autonomous and semi-autonomous agents, and the infrastructure that supports them. This is a hands-on engineering role for someone who can take AI concepts and prototypes (often built quickly in tools like AI Studio, notebooks, or third-party platforms) and turn them into reliable, scalable, well-architected production systems.

You'll work directly with client stakeholders, internal engineering teams, and technical leadership across multiple concurrent engagements. Every client brings a different stack, a different data environment, and different constraints, so this role is less about mastering one platform and more about applying strong AI/ML engineering fundamentals across generative models, retrieval systems, agentic workflows, and the model/provider landscape broadly.

What You'll Do

Generative AI Systems

• Design, build, and productionize generative AI applications (image, video, text, or audio) across a range of client environments
• Migrate prototypes built on hosted or no-code AI platforms into scalable, production infrastructure
• Build multi-user systems with job queuing, concurrency management, and status tracking for generation workloads
• Integrate and fine-tune models (LoRA/adapter training and similar approaches) and manage model versioning and deployment

Retrieval & Knowledge Systems

• Design and implement retrieval-augmented generation (RAG) or other retrieval architectures over client data sources
• Build ingestion and indexing pipelines that preserve important structural relationships in source data
• Build grounded Q&A systems that cite sources accurately and avoid answering beyond what the source material supports
• Build evaluation processes to measure accuracy, retrieval quality, and groundedness

Agents & AI Workflows

• Design and build agentic systems and multi-step AI workflows that use tools and operate reliably in production
• Apply appropriate orchestration patterns and guardrails for a given client's use case
• Build monitoring and failure-recovery mechanisms for AI systems in production

Architecture & Model Strategy

• Recommend LLMs, image/video models, and other AI providers based on quality, cost, licensing, and infrastructure fit for a given engagement
• Design architectures that keep model/provider choices swappable rather than hard-coded into the application
• Weigh in on build vs. buy, and open-weight/self-hosted vs. third-party hosted, decisions with client and internal stakeholders

Infrastructure & Client Integration

• Work across a variety of client tech stacks (commonly Python-based, but not exclusively) and both on-premises and cloud environments
• Integrate AI systems with existing client infrastructure, including proprietary APIs, internal databases, and third-party tools
• Implement access control, logging, and provenance tracking appropriate to each client's security and compliance needs
• Build monitoring, error handling, and admin tooling suitable for production systems

What We're Looking For

Required

• 5+ years of software engineering experience, with meaningful hands-on experience shipping production ML/AI systems
• Strong traditional software development background, able to read, write, and rigorously review code outside of AI-specific tooling, not just AI/ML-focused code
• Strong programming fundamentals (Python preferred) and comfort adapting to different client tech stacks
• Experience deploying generative AI models to production, including serving and concurrency
• Experience building retrieval-augmented generation (RAG) or vector search systems
• Experience building or integrating AI agents and tool-using workflows
• Working knowledge of the broader LLM and generative-model landscape, including major providers, open-weight models, and tradeoffs between them
• Ability to weigh in on architecture and model choices for a given problem, not just implement a predefined spec
• Comfortable working across multiple client environments with varying security and confidentiality requirements

Preferred

• Consulting or agency experience, or other background working across multiple concurrent client engagements
• Experience with model fine-tuning workflows (LoRA/adapters or similar)
• Experience migrating prototypes from hosted/no-code AI platforms into production infrastructure
• Experience designing systems that avoid vendor/model lock-in
• Experience with digital asset management, metadata/taxonomy design, or semantic search
• Experience building human-in-the-loop review and approval workflows for AI-generated content
• Familiarity with a range of orchestration/agent frameworks and vector databases

How You Work

• Comfortable working fully remote and collaborating effectively with distributed client and internal teams across time zones
• Comfortable ramping quickly on a new client's stack, data, and constraints
• Able to work within a defined MVP scope while designing for extensibility toward a client's broader roadmap
• Strong communicator who can document architecture clearly enough for client teams to operate and extend the system
• Comfortable with a phased delivery approach: clear MVP definition, defined acceptance criteria, incremental validation, and regular stakeholder demos

What Success Looks Like

• Client prototypes reliably migrated into stable, working production systems
• Retrieval and agentic systems that are accurate, well-grounded, and transparent about their own limitations
• Sound architecture and model recommendations that hold up as client needs evolve
• Clear documentation and knowledge transfer that let client teams operate and extend what we build