Senior AI Engineer: Agentic AI & Enterprise RAG Architect

Description

About Code Ninja

Code Ninja 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, Code Ninja 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.

Code Ninja 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 Dev Ops 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.

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:
• Lang Graph
• CrewAI
• OpenAI Agents SDK
• Auto Gen
• Semantic Kernel
• Llama Index 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
• Tru Lens
• Deep Eval
• OpenAI Evals
• Lang Smith 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:
• Lang Smith
• Langfuse
• Arize Phoenix
• Weights & Biases
• MLflow
• Experience with:
• Prompt tracing
• Token analytics
• Cost…