Senior AI Engineer – Generative AI, Agentic Systems

datavent.io

. Design and implement Python services connecting large language models to enterprise APIs, data sources and approved tools .

Posted 10/31/2026contractRemote • United Arab EmiratesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing Python services for large language models, with a strong focus on API integration, operational quality, and deployment in cloud environments. Capable of engineering workflows that ensure reliability and compliance while delivering structured outputs for enterprise applications.

Highest-signal resume keywords

Python ProgrammingAPI DesignRetrieval-Augmented Generation (RAG)Amazon BedrockCI/CD

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonAPI DesignTestingDebuggingDocument Intelligence PipelinesEvaluation DatasetsOperational QualityStructured OutputsAgent FrameworksGPU Infrastructure

Soft Skills

JudgementCommunication

Tools & Technologies

ContainersGitCI/CDAmazon BedrockAnthropic Claude

Industry Keywords

Large Language ModelsEnterprise APIsData SourcesAgentic WorkflowsAudit TrailsPrivate CloudIncident ManagementDeployment PatternsVector SearchMCP Integrations

Tech Stack

Tools & technologies

CloudERPPython

About the role

Key responsibilities & impact

Design and implement Python services connecting large language models to enterprise APIs, data sources and approved tools
Build retrieval-augmented generation systems, including document ingestion, indexing, retrieval, reranking and source attribution
Engineer agentic workflows with explicit permissions, reliable state handling, retries, audit trails and approval gates for consequential actions
Develop document intelligence pipelines producing validated structured outputs for downstream applications and ERP workflows
Create evaluation datasets and release criteria
Measure answer quality, failure modes, latency and operating cost
Deploy and operate AI applications in customer-controlled cloud or private environments
Monitor systems, handle incidents and provide clear handover documentation
Work closely with founders and customer engineering teams
Own meaningful technical decisions and shape enterprise AI engineering practices

Requirements

What you’ll need

A record of delivering production software and LLM applications, with responsibility for architecture, deployment and operational quality
Strong Python, API design, testing and debugging
Practical experience with containers, Git and CI/CD
Hands-on experience with RAG, tool use, structured outputs and evaluation beyond prompt experimentation
Experience with Amazon Bedrock, Anthropic Claude or comparable model services
Judgement to select models and deployment patterns for a use case
Ability to assess data access, untrusted inputs and agent permissions
Ability to explain trade-offs to customers and guide engineering decisions in English
Experience with self-hosted models, GPU infrastructure, vector search, MCP integrations or agent frameworks is valuable
CV, location and availability required in application
Production AI example covering contribution, evaluation approach, deployment environment and an addressed failure mode required in application
Applications must be submitted through the job portal

Benefits

Comp & perks

3-month contract
3-month probation period
1 day of annual leave
Opportunity to work closely with the founders
Ownership of meaningful technical decisions
Opportunity to shape how the team builds, evaluates and operates enterprise AI
Collaboration with customer engineering teams