About the Business Area
GBS is a group of highly skilled and talented professionals who form an essential part of ADCB's continued journey of success. With a proud history of commitment, innovation and delivery, GBS constantly strives for excellence whilst ensuring the highest standards of quality and risk awareness. Each and every member of the GBS family plays an integral role in driving ADCB's strategy, growth and digital evolution by working closely with our valued business partners to achieve exceptional customer experience through our outstanding service and support.
We are actively seeking an ambitious professional to join our team at ADCB to work alongside passionate colleagues who share your ambition to redefine excellence in banking.
In this role, your key responsibilities include:
- Engineer next-generation Conversational AI platforms (voice + chat) with Large Language Models (LLM) based agents
- Build reusable frameworks for mobile and digital channel integration
- Ensure solutions deliver natural, secure, and human-like interactions at scale
- Develop automation agents for banking operations (Know Yours Customer (KYC), compliance, customer service, risk monitoring)
- Build orchestration frameworks for multi-agent workflows and human-in-the-loop governance
- Implement retrieval-augmented generation (RAG) pipelines with vector databases and knowledge graphs
- Ensure enterprise knowledge platforms are secure, compliant, and continuously updated
- Deliver AI-driven enterprise search for employees and customers
- Design, test and refine prompts to optimize Large Language Models (LLM) performances
- Expert in advance fine tuning methods using parameter efficient tuning techniques
- Experience with Observability and Monitoring Agentic workflows
- Deep understanding in LLM evaluation metrics and frameworks
- Build and maintain AI/ML platform components: deployment, monitoring, scaling, governance
- Operationalize LLMs, embedding models, and custom ML solutions across business use cases
- Build Application Programming Interface (APIs), Software Development Kits (SDKs), and Continuous Integration (CI)/Continuous Deployment (CD) pipelines for AI integration into enterprise applications
- Implement Machine Learning Operations (MLOps) best practices for automation, retraining, monitoring, and drift management
- Establish engineering guardrails for model fairness, security, explainability, and compliance
- Ensure operational resilience, high availability, and disaster recovery for AI services
- Define and track Key Performance Indicators (KPIs) to ensure continuous improvement and effectiveness of various platforms
- Mastery in programming with Python for Generative AI applications.
- Build Artificial Intelligence (AI)/Machine Learning (ML) pipelines using Azure AI Foundry or AWS SageMaker
- Deploy and consume Azure foundation models and manage serverless deployments
The ideal candidate should have the following experience:
- At least 5+ years in leading AI/ML engineering, platform development, or large-scale digital product engineering
- Proven experience deploying AI/ML solutions at enterprise scale
- Bachelor’s degree in quantitative field such as Computer Science, Artificial Intelligence, Software Engineering, Statistics, Mathematics or related field required
- Master’s degree or equivalent in AI/ML is preferred
AI/ML Engineering
- Deep expertise in ML, deep learning, and transformer-based models
- Hands-on experience with LLMs, multimodal AI (text, vision, speech)
- Skilled in RAG, vector databases (e.g., Pinecone, FAISS), and knowledge graphs
- Familiar with agentic AI systems and orchestration frameworks
- Experience in NLP/NLU, personalization, and recommendation systems
- Proven ability to build scalable AI platforms using microservices and cloud-native tools
Programming and Tools
- Advanced Python for AI/ML engineering
- Proficient in Azure and AWS cloud platforms
- Strong SQL/NoSQL (Postgres, MongoDB, Cassandra)
- Familiar with GraphQL, REST, and FAST APIs
- Skilled in data visualization (Tableau, Power BI, Superset)
- Experience with MLOps tools (Kubernetes, Docker, MLflow, CI/CD)
- Knowledge of data lakes, vector databases, and knowledge graphs
Innovation and Collaboration
- Active in AI communities and open-source contributions
- Up-to-date with emerging AI trends (foundation models, agentic systems, RLHF)
- Rapid prototyping with focus on security, scalability, and compliance
- Strong stakeholder engagement and cross-functional collaboration
- Excellent communicator across technical and non-technical audiences