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.
RequirementsRequirements
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
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
BenefitsBenefits
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.
Skills Required
- 8–12+ years of software engineering experience
- 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, and embedding models
- Expert-level Python programming skills
- Experience building production-grade backend systems, RESTful APIs, and microservices
- Experience with asynchronous programming and scalable architectures
- Experience with FastAPI, Flask, or similar backend frameworks
- Hands-on experience designing and implementing agentic AI solutions with an agent framework
- Experience with multi-agent orchestration, planning agents, tool calling, human-in-the-loop workflows, memory management, state management, and agent collaboration
- Strong experience building enterprise retrieval-augmented generation platforms
- Experience with embedding models, vector databases, graph databases, and search technologies
- Experience with hybrid, dense, sparse, semantic, and metadata-filtered retrieval and re-ranking
- Experience designing systematic LLM evaluation frameworks
- Understanding of hallucination detection, faithfulness, answer relevancy, context precision, context recall, groundedness, toxicity, and regression testing
- Experience implementing AI guardrails, prompt-injection detection, PII masking, and content filtering
- Experience with AI observability, 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, few-shot prompting, structured prompting, function calling, JSON mode, prompt optimization, and context optimization
- Hands-on experience deploying open-source large language models
- Experience with model serving, GPU optimization, batch inference, autoscaling, multi-GPU deployment, and model quantization
- Experience with MLOps and AI platform technologies including model versioning, experiment tracking, feature stores, continuous evaluation, and continuous deployment
- Experience with at least one major cloud AI platform: Google Cloud Vertex AI, Microsoft Azure AI, AWS Bedrock, or OpenAI Azure
- Experience with PostgreSQL, Oracle, MongoDB, Redis, Elasticsearch, or OpenSearch
- Strong analytical, problem-solving, communication, stakeholder management, technical leadership, and mentoring skills
- Ability to work in Agile delivery environments
- Bachelor’s degree in computer science, artificial intelligence, information technology, engineering, or a related field
- 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 Appian, Camunda, or Pega
- Google Professional Machine Learning Engineer certification
- Microsoft Azure AI Engineer Associate certification
- AWS Certified Machine Learning Specialty certification
- Databricks Machine Learning Professional certification
- NVIDIA AI certification
- OpenAI or Anthropic ecosystem certification
What We Do
A digital transformation company, driven to solve some of the most complex problems in the world today through technology. Our core service portfolio includes staff augmentation, software development and cloud services with over 300+ projects delivered to date. Established in 2014, with a continuously growing team of 250+ experts, operating across 3 regions with Americas, Middle East, and Southeast Asia, CodeNinja is one of the fastest growing technology companies recognized by Forbes Technology Council. With value addition being a core focus for the company, CodeNinja ranks as one of the top and best reviewed software development and services company. Now offering enterprise cloud solutions to some of the leading technology companies in the world, the company has secured funding of $1.6M and is poised to invest in redefining workplace dynamics through use of advanced AI. CodeNinja has earned the trust of more than 240 clients spread across 15 different countries and 9 different industries. The company is also one of the top-rated outsourcing services provider in Pakistan, forming dedicated technology teams for some of the world's leading organizations like Microsoft, Lifeforce (Tony Robbins) and OTL alongside unicorns such as 24Seven, ABHI, and Graana. In addition, to ensure quality and commitment to client data security, the company is now ISO 270001 certified. Services: · Offshore Engineering Teams · Custom Software Development · Dedicated Development Center · Application Modernizations · Cloud Services and Solutions · Modern Workplace Solutions · Digital Transformation Strategy · AI Consulting · AR/VR and Digital Twins · eCommerce Solutions Awards and Achievements: Top 1000 Companies Clutch Global 2023 Top Company in .Net Developers 2023 Clutch Global Fall 2023 Certifications & Partnerships: Adobe Commerce Cloud Solution Partner Microsoft Solution partner for business applications. Microsoft Solution Partner for Azure Data and AI.
