About the role

Description 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. About the Role 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. Key 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. Expected Deliverables 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. Benefits Benefits What We Offer - 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. Disclaimer This job description is intended to convey information essential to understanding the scope of the role and is not exhaustive of all responsibilities, skills, or qualifications required. CodeNinja reserves the right to modify duties and responsibilities at any time.

Requirements

Required Qualifications & Skills

Experience

- 8–12+ years of experience in Software Engineering.

- 5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.

AI / Machine Learning

- Strong understanding of:

- Machine Learning

- Deep Learning

- NLP

- Transformer architectures

- Large Language Models (LLMs)

- Embedding models

Software Engineering

- Expert-level Python programming.

- Strong software engineering fundamentals.

- Experience building production-grade backend systems.

- RESTful API and microservices development.

- Async programming and scalable architectures.

- Experience with FastAPI, Flask, or similar frameworks.

Agentic AI

- Hands-on experience designing and implementing Agentic AI solutions using one or more of:

- LangGraph

- CrewAI

- OpenAI Agents SDK

- AutoGen

- Semantic Kernel

- LlamaIndex Workflows

- Experience in:

- Multi-agent orchestration

- Planning agents

- Tool calling

- Human-in-the-loop workflows

- Memory management

- State management

- Agent collaboration patterns

Retrieval-Augmented Generation (RAG)

- Strong experience building enterprise RAG platforms.

- Embedding models, such as:

- OpenAI

- Voyage AI

- BGE

- E5

- Instructor

- Cohere

- Vector databases, such as:

- Pinecone

- Qdrant

- Milvus

- Weaviate

- ChromaDB

- Graph databases, such as:

- Neo4j

- Amazon Neptune

- Memgraph

- Search technologies, including:

- Hybrid Search

- BM25

- Dense Retrieval

- Sparse Retrieval

- Semantic Search

- Metadata Filtering

- Re-ranking

- Knowledge Graph integration

LLM Evaluation

- Experience designing systematic evaluation frameworks using tools such as:

- Ragas

- TruLens

- DeepEval

- OpenAI Evals

- LangSmith Evaluation

- Understanding of:

- Hallucination detection

- Faithfulness

- Answer relevancy

- Context precision

- Context recall

- Groundedness

- Toxicity

- Regression testing

Guardrails & Observability

- Guardrails:

- Guardrails AI

- NeMo Guardrails

- OpenAI Moderation

- Prompt Injection Detection

- PII masking

- Content filtering

- Observability:

- LangSmith

- Langfuse

- Arize Phoenix

- Weights & Biases

- MLflow

- Experience with:

- Prompt tracing

- Token analytics

- Cost monitoring

- Latency monitoring

- User feedback loops

- Production debugging

Prompt Engineering

- Expertise in:

- Chain-of-Thought (CoT)

- ReAct

- Tree of Thoughts

- Self-Consistency

- Few-shot prompting

- Structured prompting

- Function Calling

- JSON mode

- Prompt optimization

- Prompt caching

- Context window optimization

- Token usage optimization

- Cost optimization

Model Deployment & Inference

- Hands-on experience deploying open-source LLMs.

- Preferred models:

- Llama

- Mistral

- Qwen

- Gemma

- DeepSeek

- Inference engines:

- vLLM

- TensorRT-LLM

- Ollama

- TGI (Text Generation Inference)

- SGLang

- Experience with:

- GPU optimization

- Batch inference

- Model serving

- Autoscaling

- Multi-GPU deployment

- Quantization (GGUF, GPTQ, AWQ, FP8, INT8, INT4)

MLOps / AI Platform

- Experience with:

- MLflow

- Kubeflow

- Docker

- Kubernetes

- GitHub Actions / GitLab CI

- Model versioning

- Experiment tracking

- Feature stores

- Continuous evaluation

- Continuous deployment

Cloud Platforms

- Experience with one or more:

- Google Cloud Platform (Vertex AI)

- Microsoft Azure AI

- AWS Bedrock

- OpenAI Azure

Database Technologies

- Experience with:

- PostgreSQL

- Oracle

- MongoDB

- Redis

- Elasticsearch / OpenSearch

Soft Skills

- Strong analytical and problem-solving skills.

- Excellent communication and stakeholder management.

- Ability to lead technical discussions and architecture reviews.

- Experience mentoring engineering teams.

- Ability to work in Agile delivery environments.

Banking & Industry Experience (Nice to Have)

- Banking or Financial Services domain experience.

- Experience with enterprise AI governance and Responsible AI frameworks.

- Knowledge of SAMA, NCA, or other financial regulatory environments.

- Experience building AI copilots and enterprise AI assistants.

- Knowledge of OCR, document intelligence, and intelligent automation.

- Experience integrating AI solutions with BPM/workflow platforms such as Appian, Camunda, or Pega.

Education & Certifications

- Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field.

- The following professional certifications are an advantage:

- 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)

Key Competencies

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.

Skills

Python

FastAPI

Flask

RESTful API development

Microservices

Agentic AI frameworks

LangGraph

CrewAI

OpenAI Agents SDK

AutoGen

Semantic Kernel

LlamaIndex

RAG platforms

Embedding models

Vector databases (Pinecone, Qdrant, Milvus, Weaviate, ChromaDB)

Knowledge graphs (Neo4j, Amazon Neptune, Memgraph)

BM25

Dense/Sparse retrieval

Semantic search

LLM evaluation frameworks (Ragas, TruLens, DeepEval, OpenAI Evals, LangSmith)

Guardrails (NeMo Guardrails, OpenAI Moderation)

LangSmith, Langfuse, Arize Phoenix, Weights & Biases

MLflow

Prompt engineering (CoT, ReAct, Tree of Thoughts, Self-Consistency, few-shot, function calling)

Token analytics

Cost monitoring

Latency monitoring

GPU optimization

Model deployment & inference (vLLM, TensorRT-LLM, Ollama, TGI, SGLang)

MLOps / AI Platform

About the role

Requirements

Responsibilities

Skills

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