Senior AI Engineer – Agentic AI Architect

CodeNinja Inc.

. Design and develop enterprise-grade AI solutions using modern LLMs and Agentic AI frameworks .

Posted 10/9/2026contractRiyadh • Saudi ArabiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing enterprise-grade AI solutions, including multi-agent systems and production-ready RAG platforms. Proficient in Python programming, machine learning, and deploying open-source LLMs while ensuring compliance with security and governance standards.

Highest-signal resume keywords

Expert-Level Python ProgrammingExperience with Large Language Models (LLMs)Production-Grade Backend Systems DevelopmentMulti-Agent Orchestration and PlanningExperience with Google Cloud Platform (Vertex AI)

ATS Keywords

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Hard Skills

Artificial IntelligenceMachine LearningDeep LearningNLPTransformer ArchitecturesRESTful API DevelopmentAsync ProgrammingPrompt Engineering TechniquesGPU OptimizationEmbedding Models

Soft Skills

Analytical SkillsProblem-Solving SkillsExcellent CommunicationStakeholder ManagementMentoring Engineering Teams

Tools & Technologies

FastAPIFlaskMLflowKubeflowDockerKubernetesGitHub ActionsGoogle Cloud PlatformMicrosoft Azure AIAWS Bedrock

Industry Keywords

Enterprise AI SolutionsMulti-Agent SystemsRAG PlatformsAI Best PracticesAgile Delivery Environments

Tech Stack

Tools & technologies

AWSAzureCloudDockerElasticSearchFlaskGoogle Cloud PlatformKubernetesMicroservicesMongoDBOraclePostgresPythonReactRedis

About the role

Key responsibilities & impact

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
Independently design and deliver 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, and secure, scalable, cost-optimized AI deployments

Requirements

What you’ll need

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 Machine Learning, Deep Learning, NLP, Transformer architectures, Large Language Models (LLMs), and embedding models
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
Hands-on experience designing and implementing Agentic AI solutions using LangGraph, CrewAI, OpenAI Agents SDK, AutoGen, Semantic Kernel, or LlamaIndex Workflows
Experience in multi-agent orchestration, planning agents, tool calling, human-in-the-loop workflows, memory management, state management, and agent collaboration patterns
Strong experience building enterprise RAG platforms
Experience with embedding models, vector databases, graph databases, and search technologies
Experience designing systematic LLM evaluation frameworks
Understanding of hallucination detection, faithfulness, answer relevancy, context precision, context recall, groundedness, toxicity, and regression testing
Experience with guardrails and observability, including prompt tracing, token analytics, cost monitoring, latency monitoring, user feedback loops, and production debugging
Expertise in prompt engineering techniques including Chain-of-Thought, 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, and cost optimization
Hands-on experience deploying open-source LLMs
Experience with GPU optimization, batch inference, model serving, autoscaling, multi-GPU deployment, and quantization
Experience with MLflow, Kubeflow, Docker, Kubernetes, GitHub Actions / GitLab CI, model versioning, experiment tracking, feature stores, continuous evaluation, and continuous deployment
Experience with Google Cloud Platform (Vertex AI), Microsoft Azure AI, AWS Bedrock, or OpenAI Azure
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
Experience mentoring engineering teams
Ability to work in Agile delivery environments
Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field

Benefits

Comp & perks

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