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Senior AI Engineer / Agentic AI Architect - Riyadh, Hybrid
Senior AI Engineer and Agentic AI Architect designing secure, scalable LLM and RAG platforms for CodeNinja’s banking and financial services clients. Leads production AI architecture, delivery, optimization, and engineering-team mentorship.
विवरण
- Create enterprise AI solutions with modern LLMs and agentic AI frameworks
- Architect multi-agent systems for planning, reasoning, tool use, and workflow orchestration
- Develop production-ready RAG platforms across structured and unstructured enterprise data
- Build scalable APIs and AI services with Python and contemporary backend frameworks
- Establish evaluation frameworks for LLM quality, safety, and performance
- Improve AI application latency, throughput, and infrastructure efficiency
- Deploy and operate open-source LLMs in production
- Partner with architects, product owners, business analysts, and DevOps teams on enterprise AI platforms
- Apply enterprise security, governance, and responsible AI standards
- Mentor engineering teams and advance reusable AI practices and frameworks
- Independently deliver enterprise AI platforms, multi-agent systems, RAG knowledge assistants, AI copilots, LLM evaluation frameworks, production AI APIs, observability solutions, and secure, scalable, cost-efficient deployments
आवश्यकताएँ
- 8–12+ years of software engineering experience
- 5+ years of hands-on experience in artificial intelligence, machine learning, and generative AI
- Strong knowledge of machine learning, deep learning, NLP, transformer architectures, LLMs, and embedding models
- Expert Python programming skills
- Strong software engineering fundamentals
- Experience developing production-grade backend systems
- Experience with RESTful APIs and microservices
- Experience with asynchronous programming and scalable architectures
- Experience with FastAPI, Flask, or comparable frameworks
- Hands-on experience designing and implementing agentic AI solutions with LangGraph, CrewAI, OpenAI Agents SDK, AutoGen, Semantic Kernel, or LlamaIndex Workflows
- Experience with multi-agent orchestration, planning agents, tool calling, human-in-the-loop workflows, memory and state management, and agent collaboration patterns
- Strong experience building enterprise RAG platforms
- Experience with embedding models, vector and graph databases, and search technologies
- Experience creating systematic LLM evaluation frameworks
- Understanding of hallucination detection, faithfulness, answer relevance, context precision and recall, groundedness, toxicity, and regression testing
- Experience implementing guardrails and observability, including prompt tracing, token and cost analytics, latency monitoring, feedback loops, and production debugging
- Expertise in prompt engineering, including Chain-of-Thought, ReAct, Tree of Thoughts, Self-Consistency, few-shot and structured prompting, function calling, JSON mode, prompt optimization and caching, context-window optimization, token efficiency, 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 or 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, or OpenSearch
- Strong analytical and problem-solving abilities
- Excellent communication and stakeholder-management skills
- 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
लाभ
- Compensation is based on experience and qualifications
- Work on enterprise-scale technology and digital transformation initiatives
- Exposure to banking, financial services, AI, data, and emerging technology environments
- Professional development and learning opportunities
- Collaborative, technically focused work environment
- Work alongside experienced technology and consulting professionals
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