Mid Level 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 10/22/2026contractRemote • EgyptMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

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

Highest-signal resume keywords

LLM Application DevelopmentAWS and Azure AI ServicesPython ProgrammingRAG Pipeline DevelopmentCI/CD and Infrastructure as Code

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Workflow DesignAPI DevelopmentAgentic Systems BuildingData EngineeringPrompt EngineeringModel Fine-TuningContainerizationVector Database ExperienceMCP Server IntegrationMulti-Agent Protocols

Soft Skills

Strong Communication SkillsMentoring and Leadership

Tools & Technologies

DockerKubernetesTerraformBicepCloudFormationOpenSearchAzure AI SearchPgvectorPineconeGit

Certifications & Qualifications

AWS Certified AI PractitionerAWS Solutions ArchitectAzure AI-102Azure AZ-204Azure AZ-400

Industry Keywords

Regulated Sector ExperienceBankingHealthcareGovernment

Tech Stack

Tools & technologies

AWSAzureCloudDockerKubernetesPythonSparkTerraform

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 the appropriate platform and model for each use case
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, including regression tests, LLM-as-judge methods and human review
Add responsible-AI controls including prompt-injection defences, content filtering, PII handling, access control and audit logging
Containerise and deploy services with CI/CD and infrastructure as code
Add observability through tracing, logging, latency and token-cost monitoring
Optimise performance, reliability and cost across models, prompts and infrastructure
Mentor engineers, run design and code reviews, and document architectures and decisions
Collaborate with product owners, data teams, security and architecture to take 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)
3+ years of software engineering experience, with 1+ years building LLM or GenAI applications in production
Strong Python skills, plus experience building and operating APIs and services
Hands-on experience building agentic systems with a modern framework (for example 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 (for example OpenSearch, Azure AI Search, pgvector or Pinecone)
Experience with cloud fundamentals: containers (Docker, Kubernetes or serverless), networking and identity (IAM / Entra ID), and secrets management
Experience with CI/CD, Git and infrastructure as code (Terraform, Bicep or CloudFormation)
Understanding of LLM evaluation, prompt engineering, and safety and security risks specific to AI systems
Strong communication skills and the ability to explain trade-offs to technical and non-technical stakeholders
Experience with MCP servers, multi-agent protocols (for example A2A), or agent orchestration at scale
Fine-tuning or model customization, and experience with open-source models
Background in a regulated sector such as banking, healthcare or government
Cloud certifications (for example AWS Certified AI Practitioner or Solutions Architect, Azure AI-102 or AZ-204, AZ-400)
Experience with data engineering (Spark, Databricks, Fabric or Glue) and data governance