Senior AI Developer
New
O
Octopus by RTGArtificial intelligence
Cairo, Cairo Governorate, EgyptContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of software engineering experience, with 2+ years building LLM or GenAI applications in production.
- Required Skills
- PythonCI/CDLangChain
Requirements
- Hold a bachelor's degree in Computer Science, Engineering or a related field, or have equivalent experience.
- Have 5+ years of software engineering experience, including 2+ years building LLM or GenAI applications in production.
- Demonstrate strong Python skills and experience building and operating APIs and services.
- Have hands-on experience building agentic systems with a modern framework such as LangGraph, LangChain, Microsoft Agent Framework / Semantic Kernel, AutoGen, Strands Agents or CrewAI.
- Have production experience with at least one of AWS or Azure AI services and working knowledge of the other.
- Have practical RAG experience with vector databases or search services such as OpenSearch, Azure AI Search, pgvector or Pinecone.
- Understand cloud fundamentals, including containers, networking, identity and secrets management.
- Have experience with CI/CD, Git and infrastructure as code such as Terraform, Bicep or CloudFormation.
- Understand LLM evaluation, prompt engineering, and safety and security risks specific to AI systems.
- Communicate effectively and explain trade-offs to technical and non-technical stakeholders.
- Nice to have: experience with MCP servers, multi-agent protocols, agent orchestration at scale, fine-tuning, model customization or open-source models.
- Nice to have: experience in a regulated sector, cloud certifications, data engineering or data governance, or mentoring or leading small teams.
Responsibilities
- Design and build single- and multi-agent workflows with planning, tool use, memory and human-in-the-loop controls.
- Develop LLM applications on AWS and Azure, and recommend platforms and models for each use case.
- Build RAG pipelines covering data ingestion, chunking, embeddings, vector search and grounding over enterprise data.
- Integrate agents with internal systems, APIs and SaaS tools using MCP and function or tool calling.
- Implement evaluation frameworks for quality, accuracy, safety and cost, including regression tests, LLM-as-judge methods and human review.
- 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, and add observability for tracing, logging, latency and token costs.
- Optimize performance, reliability and cost across models, prompts and infrastructure.
- Mentor engineers, run design and code reviews, and document architectures and decisions.
- Work with product owners, data teams, security and architecture to take AI use cases from idea to production.
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