Artificial Intelligence Engineer

Opportunity Description AI Engineer

Our end client a technology-driven organization focused on data engineering, AI, cloud solutions, and digital transformation delivers innovative technology solutions that help businesses modernize their data infrastructure, leverage artificial intelligence, and build scalable digital platforms. The organization works on advanced technology initiatives across enterprise and data-intensive environments.

They are looking for an experienced AI Engineer to design, develop, and deploy intelligent AI/ML solutions, working with modern AI technologies to solve complex business challenges. This is a high-impact opportunity for professionals who enjoy building production-ready AI systems, working with cross-functional teams, and contributing to next-generation AI-driven solutions.

Role & responsibilities

• Build and own sub-agents: typed input/output schemas, versioned prompts, a

constrained tool set, and a published success metric for each

• Design the orchestration layer planning, replanning, tool-failure recovery, and

handoff between supervisor and sub-agents

• Build the eval harness that decides when an agent is safe to promote from shadow

to assisted to autonomous

• Work the two-tier model setup: a heavy model for judgment and classification, a

lighter self-hosted tier for high-volume extraction and formatting

• Make agent behavior observable and debuggable traces, cost per agent, failure

attribution

Preferred candidate profile

• You've shipped LLM systems into production, not just demos. You can talk about

what broke and what you changed.

• Comfortable with agent orchestration in some form LangGraph, custom runtimes, workflow engines, or your own. We care more about the reasoning than the library.

• Strong Python. Go or TypeScript useful.

• You've built evals, or you've felt the pain of not having them.

• Solid on structured output, schema validation, and getting reliable behavior out of unreliable models.

Nice to have

• Durable execution experience Temporal, Cadence, Step Functions, or similar

• Self-hosted inference: vLLM, TGI, quantisation, GPU scheduling

• Retrieval systems at scale, and knowing when not to use them

• Document understanding: OCR, layout parsing, extraction from messy PDFs

• Anything regulated audit trails, approval workflows, compliance reporting