Applied AI Engineer - Operations

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SeniorZdalnieFull-time

#470605·Dodano 6 dni temu·2

Źródło: Action1

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Tech Stack / Keywords

LLMRAGPython JavaScript REST APIswebhooksJSONOAuth AWS Salesforce

Podsumowanie oferty

As a Senior Applied AI Engineer in Operations, you’ll turn business workflows into reliable AI-powered tools and automations for teams including Support, Finance, Sales Ops, HR, IT, and Legal. You’ll build and improve production AI workflows using LLMs and business-system integrations, with Python or JavaScript.

Wymagania

4+ years in operations engineering, business systems, automation, or a similar role, including 1+ years building production LLM or AI-enabled workflows.
Hands-on experience with workflow automation platforms and REST APIs, webhooks, JSON, and OAuth.
Strong working knowledge of prompt design, retrieval-augmented generation (RAG), and agent/tool-use patterns.
Proficiency in Python or JavaScript, and comfort with cloud services; AWS preferred.
Experience with at least one major business system, such as NetSuite, Salesforce, or ServiceNow.
Ability to translate business needs into technical solutions and explain trade-offs to non-technical leaders.
Sound judgment and practical knowledge of AI security, privacy, and data governance, including least-privilege access, sensitive-data protection, data minimization, and approval controls for sensitive actions.

Nice to have:

Experience with Microsoft 365 / Teams integrations and Microsoft Graph.
Background in finance, revenue, or support operations processes.
Familiarity with process mining, BI tools, or SQL.
Experience with SOC 2, ISO 27001, or similar compliance frameworks.
Relevant AWS, Azure, or security certifications.
Familiarity with IT management, RMM, or endpoint security concepts.
Experience shipping software with coding agents such as Claude Code or Codex, including harnesses, development loops, reusable skills, and context and specification management.
Hands-on ownership of production backend services/APIs, with practical database and container experience.
Systematic AI evaluation using datasets, metrics, baselines, reproducible experiments, and regression detection.
Deeper ML foundations, statistical experimentation, and model training or fine-tuning.
Experience with runtime isolation and sandboxing for sensitive execution.

Obowiązki

Work with teams to understand workflows, identify bottlenecks, and propose improvements; agree on process changes with the COO and relevant teams before implementation.
Build AI tools and automations that connect business systems and handle tasks such as document processing or ticket triage; deliver working versions quickly and improve them through feedback.
Prioritize opportunities by time saved, error reduction, and risk; measure benefits, cost reduction, and adoption.
Run and support solutions: test AI outputs, keep integrations reliable as systems change, and manage failures, latency, and cost.
Protect customer and employee data through limited access, minimal exposure, and redaction before model calls; work with IT and Security on safeguards, audit trails, and human review where needed.
Evaluate AI tools and vendors, and recommend when to use an existing solution versus building one.
Document workflows and runbooks, train teams to use the solutions, and help them identify and request further automations.

What Success Looks Like:

First 30 days: Understand key workflows and systems, and deliver a prioritized automation backlog.
First 90 days: Ship 3–5 production automations or integrations with measured impact, and publish AI usage and data-handling standards.
First year: Establish a repeatable intake-to-deployment process, show gains in hours saved and error reduction, and make AI tooling a trusted part of daily operations.

How We Measure Impact:

Hours saved, cycle-time reduction, error and rework rates, automation reliability (uptime and failure rate), AI cost per task, and stakeholder satisfaction.

Benefity

Flexible working hours.
Opportunities for career advancement.

Elastyczne godziny

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