Senior AI Developer

Robusta Studio

. Design and build single- and multi-agent AI workflows with planning, tool use, memory and human-in-the-loop controls .

Posted 10/15/2026contractRemote • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building AI workflows, particularly with LLM applications on AWS and Azure. Proficient in integrating systems, optimizing performance, and implementing responsible AI controls while mentoring engineering teams.

Highest-signal resume keywords

LLM Application DevelopmentAWS and Azure AI ServicesPython ProgrammingRAG Pipeline DevelopmentAPI and Service Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringAgentic Systems DevelopmentPrompt EngineeringData Ingestion and ChunkingModel CustomizationContainerizationCI/CD ImplementationInfrastructure as CodeVector Database ExperienceMCP Server Management

Soft Skills

Strong Communication SkillsMentoring and Leadership

Tools & Technologies

LangGraphLangChainMicrosoft Agent FrameworkOpenSearchAzure AI SearchPineconeGitFunction/Tool CallingObservability ToolsAudit Logging

Certifications & Qualifications

Cloud Certifications

Industry Keywords

AI WorkflowsHuman-in-the-Loop ControlsQuality and Safety EvaluationData GovernanceRegulated Sectors

Tech Stack

Tools & technologies

AWSAzureCloudPython

About the role

Key responsibilities & impact

Design and build single- and multi-agent AI workflows with planning, tool use, memory and human-in-the-loop controls
Develop LLM applications on AWS and Azure AI platforms and recommend platforms and models for use cases
Build RAG pipelines covering data ingestion, chunking, embeddings, vector search and enterprise-data grounding
Integrate agents with internal systems, APIs and SaaS tools using MCP and function/tool calling
Implement evaluation frameworks for quality, accuracy, safety and cost
Add guardrails and responsible-AI controls, including prompt-injection defenses, content filtering, PII handling, access control and audit logging
Containerize and deploy services with CI/CD and infrastructure as code
Add observability through tracing, logging, latency and token-cost monitoring
Optimize model, prompt and infrastructure performance, reliability and cost
Mentor engineers, conduct design and code reviews, and document architectures and decisions
Collaborate with product owners, data teams, security and architecture to move AI use cases from idea to production

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering or a related field (or equivalent experience)
5+ years of software engineering experience
2+ years building LLM or GenAI applications in production
Strong Python skills
Experience building and operating APIs and services
Hands-on experience building agentic systems with a modern framework, such as LangGraph, LangChain, Microsoft Agent Framework / Semantic Kernel, AutoGen, Strands Agents or CrewAI
Production experience on at least one of AWS or Azure AI services, and working knowledge of the other
Practical RAG experience with vector databases or search services, such as OpenSearch, Azure AI Search, pgvector or Pinecone
Experience with containers, networking, identity, secrets management, CI/CD, Git and infrastructure as code
Understanding of LLM evaluation, prompt engineering, and AI-specific safety and security risks
Strong communication skills and ability to explain trade-offs to technical and non-technical stakeholders
Experience with MCP servers, multi-agent protocols, agent orchestration at scale, fine-tuning, model customization, open-source models, regulated sectors, data engineering, data governance, or mentoring/leading small teams are nice to have
Cloud certifications are nice to have