Senior AI Engineer / Agentic AI Architect

CodeNinja

Riyadh, Saudi Arabia
Lead/Staff
8+ years
Bachelor's degree
Posted 30 Sep 2026

Skills

Required

Agentic AI
Machine Learning
Software Engineering
OpenAI
Microservices / Cloud AI Platforms / Responsible AI
Artificial Intelligence
Python
Large Language Models LLMs
Databases
FastAPI
Flask
PostgreSQL
MongoDB
Elasticsearch / OpenSearch

Preferred

AI
BPM / workflow
MLflow
Microsoft Azure AI
OpenAI Azure
enterprise AI governance
SAMA

In this role, you will deliver production-grade AI applications with a focus on scalability, security, observability, governance, and performance. You will work across Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.

The ideal candidate will have strong software engineering fundamentals and deep expertise in LLMs, RAG, Agentic AI, and modern AI engineering practices. They will have 8–12+ years in software engineering, including 5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.

About CodeNinja

CodeNinja is a global software and AI infrastructure company delivering full-stack technology solutions across AI, software engineering, data, and digital transformation.

With operations across Saudi Arabia and global technology hubs, CodeNinja works with organizations across multiple industries to deliver technology solutions that support business transformation and innovation.

Our teams work across areas including AI, software engineering, data and analytics, cloud, enterprise technology, and digital transformation.

CodeNinja is looking for an experienced Senior AI Engineer / Agentic AI Architect to design, build, and deploy enterprise-scale AI solutions for the banking and financial services sector.

TruLens

Responsibilities

Design and develop enterprise-grade AI solutions using modern LLMs and Agentic AI frameworks.
Architect multi-agent systems capable of planning, reasoning, tool usage, and workflow orchestration.
Build production-ready RAG platforms integrating structured and unstructured enterprise data.
Design scalable APIs and AI services using Python and modern backend frameworks.
Implement robust evaluation frameworks for LLM quality, safety, and performance.
Optimize AI applications for latency, throughput, and infrastructure cost.
Deploy and manage open-source LLMs in production environments.
Collaborate with architects, product owners, business analysts, and DevOps teams to deliver enterprise AI platforms.
Ensure compliance with enterprise security, governance, and responsible AI practices.
Mentor engineering teams and contribute to AI best practices and reusable frameworks.
The selected candidate should be capable of independently designing and delivering:
Enterprise AI platforms
Multi-agent AI systems
RAG-based knowledge assistants
AI copilots
LLM evaluation frameworks
Production-ready AI APIs
AI observability and monitoring solutions
Secure, scalable, and cost-optimized AI deployments suitable for enterprise production environments.
Deep Learning
NLP
Transformer architectures
Large Language Models (LLMs)
Embedding models
RESTful API and microservices development.
LangGraph
CrewAI
OpenAI Agents SDK
AutoGen
Semantic Kernel
LlamaIndex Workflows
Planning agents
Tool calling
Human-in-the-loop workflows
Memory management
Agent collaboration patterns
Strong experience building enterprise RAG platforms.
Embedding models, such as:
Instructor
Cohere
Vector databases, such as:
Qdrant
Milvus
Weaviate
ChromaDB
Graph databases, such as:
Semantic Search
Re-ranking
Knowledge Graph integration
Experience designing systematic evaluation frameworks using tools such as:
Answer relevancy
Context precision
Context recall
Regression testing
Guardrails:
NeMo Guardrails
PII masking
Observability:
Prompt tracing
Token analytics
Cost monitoring
Latency monitoring
User feedback loops
Production debugging
Chain-of-Thought (CoT)
ReAct
Tree of Thoughts
Self-Consistency
Structured prompting
Function Calling
JSON mode
Prompt optimization
Prompt caching
Context window optimization
Token usage optimization
Hands-on experience deploying open-source LLMs.
Preferred models:
Llama
Mistral
Qwen

Requirements

8–12+ years of experience in Software Engineering.
5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.

Strong understanding of

Expert-level Python programming.
Strong software engineering fundamentals.
Experience building production-grade backend systems.
Async programming and scalable architectures.
Experience with FastAPI, Flask, or similar frameworks.
Hands-on experience designing and implementing Agentic AI solutions using one or more of:
Experience in:
Multi-agent orchestration
State management
OpenAI
Voyage AI
Memgraph
Search technologies, including:
Expertise in:
AWS Bedrock
Experience with:
PostgreSQL
Oracle
MongoDB
Redis

Elasticsearch / OpenSearch

Strong analytical and problem-solving skills.
Excellent communication and stakeholder management.
Ability to lead technical discussions and architecture reviews.
Ability to work in Agile delivery environments.
Experience building AI copilots and enterprise AI assistants.
Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field.
Google Professional Machine Learning Engineer
Microsoft Azure AI Engineer Associate
AWS Certified Machine Learning – Specialty
Databricks Machine Learning Professional
NVIDIA AI Certifications
OpenAI or Anthropic ecosystem certifications (where applicable)
Agentic AI | Multi-Agent Systems | Large Language Models (LLMs) | RAG | Vector & Graph Databases | LLM Evaluation | Guardrails & Observability | Prompt Engineering | LLM Deployment & Inference | MLOps | Python | APIs & Microservices | Cloud AI Platforms | Responsible AI

Preferred

Machine Learning
Prompt Injection Detection
MLflow
Experience with:
MLflow
Experience with one or more:
Google Cloud Platform (Vertex AI)
Microsoft Azure AI

OpenAI Azure

Experience mentoring engineering teams.
Banking or Financial Services domain experience.
Experience with enterprise AI governance and Responsible AI frameworks.
Knowledge of SAMA, NCA, or other financial regulatory environments.
Knowledge of OCR, document intelligence, and intelligent automation.
Experience integrating AI solutions with BPM/workflow platforms such as Appian, Camunda, or Pega.
The following professional certifications are an advantage:

Benefits

Cost optimization

Competitive compensation based on experience and qualifications.
Opportunity to work on enterprise-scale technology and digital transformation projects.
Exposure to banking, financial services, AI, data, and emerging technology environments.
Professional growth and learning opportunities.
Collaborative and technically driven work environment.
Opportunity to work with experienced technology and consulting professionals.