About The Position

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team, engineering experiences of tomorrow. We are placing senior AI engineers to design and deliver production agentic AI systems for an enterprise client.

Requirements

  • 3+ years building with LLMs in production systems that real users depend on
  • Hands-on experience designing agentic workflows: tool and function calling, retrieval-augmented generation (RAG), multi-step orchestration, and memory
  • Deep practical experience with the OpenAI API and GPT Enterprise: model selection, prompt engineering, structured outputs, function calling, cost and latency tuning, and data-handling controls
  • Strong prompt-engineering discipline and an evaluation-first approach, measuring agent quality with evals rather than by inspection
  • Direct, hands-on production experience with the Glean platform: Agent Builder, Apps, Actions, Model Hub, connectors, and permission-aware retrieval (non-negotiable)
  • Working knowledge of enterprise search and RAG architecture on Glean: indexing, connectors, retrieval quality, and runtime permission enforcement
  • Production experience deploying and operating services on AWS and/or GCP, with comfort across both preferred. IAM, secrets management, networking, and cost awareness
  • Strong software engineering fundamentals in Python or equivalent: clean, tested, maintainable code, API design, CI/CD, and version control
  • Experience integrating enterprise SaaS systems through API (for example Jira, Slack, Google Workspace, ticketing and GRC tools), including auth flows and webhook and event patterns
  • Data engineering competence: moving and transforming data between systems, working with warehouses, and handling sensitive data securely

Nice To Haves

  • Experience locking down and governing enterprise tools, including controlled intake, permission models, and audit trails
  • Familiarity with observability and incident response for AI systems: monitoring drift, alerting, and root-cause analysis
  • Exposure to regulated or finance and accounting environments where auditability and data governance matter
  • Prior consulting or client-facing delivery experience and comfort working embedded with a client’s teams

Responsibilities

  • Build autonomous agents on the client’s existing enterprise AI stack
  • Own agents from design through production, monitoring, and iteration
  • Approach agents as production software
  • Work in terms of triggers, retrieval, reasoning steps, actions, permissions, evaluation, and observability, and know how to make an agent reliable, secure, and maintainable at enterprise scale
  • Build for maintainability, favoring configuration over custom code and shared services over one-offs, keeping the bespoke surface area small
  • Ensure security and governance by default, handling credentials, permissions, and sensitive data correctly without being prompted, and designing for audit from the start
  • Be pragmatic and scale-aware, designing around real platform limits such as tool-call budgets, payload caps, and rate limits before they become production problems
  • Provide client-ready communication, able to explain an architecture decision to a technical buyer, defend it under questioning, and write clearly

Benefits

  • Healthcare
  • Basic Life Insurance
  • Short and Long-term disability insurance according to the Company’s Benefit Plans
  • Internal events (meetups, conferences, workshops)
  • Udemy access
  • Language courses
  • Company-paid certifications
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