Senior AI Engineer / Agentic AI Architect
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Before you apply
LocationLahore, Punjab, Pakistan
Typecontract
SenioritySenior
Experience8–12 yrs
DepartmentProfessional Services
Company size201–500 people
PostedOct 1, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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
Strong understanding of Deep Learning
Strong understanding of NLP
Strong understanding of Transformer architectures
Strong understanding of Large Language Models (LLMs)
Strong understanding of 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 one or more of: LangGraph, CrewAI, OpenAI Agents SDK, AutoGen, Semantic Kernel, LlamaIndex Workflows
Experience in Multi-agent orchestration
Experience in Planning agents
Experience in Tool calling
Experience in Human-in-the-loop workflows
Experience in Memory management
Experience in State management
Experience in Agent collaboration patterns
Strong experience building enterprise RAG platforms
Experience designing systematic evaluation frameworks using tools such as: Ragas, TruLens, DeepEval, OpenAI Evals, LangSmith Evaluation
Understanding of Hallucination detection
Understanding of Faithfulness
Understanding of Answer relevancy
Understanding of Context precision
Understanding of Context recall
Understanding of Groundedness
Understanding of Toxicity
Understanding of Regression testing
Experience with Prompt tracing
Experience with Token analytics
Experience with Cost monitoring
Experience with Latency monitoring
Experience with User feedback loops
Experience with Production debugging
Expertise in Chain-of-Thought (CoT)
Expertise in ReAct
Expertise in Tree of Thoughts
Expertise in Self-Consistency
Expertise in Few-shot prompting
Expertise in Structured prompting
Expertise in Function Calling
Expertise in JSON mode
Expertise in Prompt optimization
Expertise in Prompt caching
Expertise in Context window optimization
Expertise in Token usage optimization
Expertise in Cost optimization
Hands-on experience deploying open-source LLMs
Experience with GPU optimization
Experience with Batch inference
Experience with Model serving
Experience with Autoscaling
Experience with Multi-GPU deployment
Experience with Quantization (GGUF, GPTQ, AWQ, FP8, INT8, INT4)
Experience with MLflow
Experience with Kubeflow
Experience with Docker
Experience with Kubernetes
Experience with GitHub Actions / GitLab CI
Experience with Model versioning
Experience with Experiment tracking
Experience with Feature stores
Experience with Continuous evaluation
Experience with Continuous deployment
Experience with one or more: Google Cloud Platform (Vertex AI), Microsoft Azure AI, AWS Bedrock, OpenAI Azure
Experience with PostgreSQL
Experience with Oracle
Experience with MongoDB
Experience with Redis
Experience with 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
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
Skills
Python
FastAPI
Flask
LangGraph
CrewAI
OpenAI Agents SDK
AutoGen
Semantic Kernel
LlamaIndex Workflows
OpenAI
Voyage AI
BGE
E5
Instructor
Cohere
Pinecone
Qdrant
Milvus
Weaviate
ChromaDB
Neo4j
Amazon Neptune
Memgraph
Ragas
TruLens
DeepEval
OpenAI Evals
LangSmith Evaluation
Guardrails AI
NeMo Guardrails
OpenAI Moderation
Langfuse
Arize Phoenix
Weights & Biases
MLflow
Llama
Mistral
Qwen
Gemma
DeepSeek
vLLM
TensorRT-LLM
Ollama
TGI
SGLang
Docker
Kubernetes
GitHub Actions
GitLab CI
PostgreSQL
Oracle
MongoDB
Redis
Elasticsearch
OpenSearch
Google Cloud Platform
Vertex AI
Microsoft Azure AI
AWS Bedrock
OpenAI Azure
Appian
Camunda
Pega
Benefits
Competitive compensation
Professional growth
Collaborative environment
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.
Requirements
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
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
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.