Senior AI Engineer

This is a remote position.

About the Company

Our client is a specialized AI engineering partner that helps large organizations turn AI from slideware into shipped, production systems. They build AI-powered “digital teammates” that plug into real workflows, handle real data, and are measured on real business outcomes.

They work with established enterprises across sectors such as financial services, healthcare, retail, energy, education, and manufacturing, typically in environments where reliability, security, and scale really matter. Their teams are intentionally small, senior, and execution-focused, with most engineers bringing well over a decade of experience in software and data.

The culture is hands-on and delivery-oriented: strong engineering fundamentals, pragmatic architecture, and a bias toward systems that can be deployed, observed, and improved—not just demoed.

Role Overview

Our client is hiring a Senior AI Engineer to own the design and implementation of AI systems that actually run in production, not just in notebooks. This is a role for someone who can take a loosely defined business problem, shape a solution, and drive it all the way through architecture, implementation, and deployment.

You will be embedded in a cross-functional delivery team, collaborating with product, design, and client stakeholders. You’ll design and implement models, build the surrounding data and orchestration layers, and ensure the resulting systems are observable, reliable, and cost-effective at scale.

What You’ll Do

• Design, implement, and deploy AI/ML systems end-to-end, from prototypes to hardened production services
  

• Build and maintain data pipelines, retrieval layers, and training/inference workflows that support LLM and other model types
  

• Develop and evolve retrieval-augmented generation (RAG) setups, including chunking strategies, embedding selection, and vector search design
  

• Implement and tune agentic / multi-step workflows that orchestrate tools, APIs, and models to complete complex tasks
  

• Add observability around AI behavior: evaluations, logging, metrics, and guardrails to monitor quality, drift, and failures
  

• Integrate models with existing application backends and APIs, and design clean interfaces for internal and external consumers
  

• Optimize systems for performance and cost (token usage, caching strategies, routing between models, etc.)
  

• Contribute to architecture decisions, code reviews, and technical strategy within your team
  

Requirements

What You Bring

• Over 4 years of professional software engineering experience, with at least 2 years working on AI/ML features in production environments
  

• Strong programming skills in Python (or similar languages) and solid software engineering fundamentals
  

• Hands-on experience building with modern LLMs and generative AI tooling (prompting, fine-tuning, RAG, or agents)
  

• Experience with at least one major cloud platform; depth with AWS is a plus
  

• Familiarity with AI/LLM orchestration frameworks and evaluation tools
  

• Experience designing and integrating REST or gRPC APIs
  

• Working knowledge of containerization and orchestration (Docker, Kubernetes)
  

• Understanding of security and safety concerns around AI systems (prompt injection, PII handling, access control, etc.)
  

• Comfort working in ambiguous problem spaces and making pragmatic tradeoffs under constraints
  

Nice To Have

• Experience running AI systems at enterprise scale (SLAs, compliance, multi-tenant environments)
  

• Background with ML observability and experimentation platforms
  

• Prior work in cost-optimization for LLM-heavy workloads (model selection, caching, quantization, or routing strategies)
  

• Day-to-day use of AI-assisted coding tools in your workflow
  

How You Work

• You like to ship. You’d rather deliver a robust v1, measure it, and iterate, than polish an endless prototype
  

• You’re direct and clear in communication, and you value honest feedback that makes the work better
  

• You take ownership from end to end: design, implementation, testing, deployment, and support
  

• You care about craft: naming, tests, observability, and thoughtful tradeoffs matter to you
  

• You’re energized by working with other senior people who set a high bar and expect the same from you