AI Engineering Lead – Generative AI Solutions

• *AI Engineering Lead – Generative AI Solutions**

• *Experience:** 15\+ Years in Software Engineering
• *Relevant Experience:** 8\+ Years in AI/ML, Data Engineering, or Cloud\-Native Development
• *Employment Type:** Full\-time, Permanent
• *Work Mode:** On\-site
• *Number of Openings:** 6

• *Job Description**

We are hiring an experienced AI Engineering Lead to lead the design, development, and production deployment of enterprise\-grade Generative AI and Agentic AI solutions.

The ideal candidate will have strong hands\-on software engineering expertise, deep knowledge of LLM\-based applications, and proven experience in technical leadership, cloud\-native engineering, AI security, and production readiness.

This is a full\-time, permanent opportunity based in Abu Dhabi, UAE. The selected candidate will be required to work on\-site at the project location and must be willing to relocate if currently based elsewhere.

• *Key Responsibilities**

• *1\. Agentic AI Solution Design**

• Design and develop agentic AI solutions using LLMs, RAG, LangChain, LangGraph, MCP, and multi\-agent orchestration.
• Build LLM workflows, supervisor/worker architectures, tool calling, and document intelligence solutions.
• Develop reusable AI components and scalable enterprise AI architectures.

• *2\. Hands\-on Engineering**

• Develop and review production\-grade Python applications and AI services.
• Build backend APIs using FastAPI and related frameworks.
• Establish coding standards, testing practices, and code review guidelines.
• Work hands\-on with complex technical implementations and troubleshoot engineering challenges.

• *3\. Production Deployment and Cloud Engineering**

• Build and deploy containerized AI applications and microservices on AWS.
• Work with Kubernetes, Docker, API gateways, managed identity, and secure networking.
• Support CI/CD automation, deployment pipelines, and production readiness.
• Optimize application performance, scalability, reliability, and operating costs.

• *4\. LLM Evaluation and Quality Assurance**

• Develop evaluation frameworks using golden datasets, regression tests, and automated quality checks.
• Measure accuracy, groundedness, latency, and token consumption.
• Implement automated testing, quality gates, and release validation.
• Monitor production performance and continuously improve AI application quality.

• *5\. AI Security and Responsible AI**

• Implement guardrails against prompt injection, data leakage, and unsafe tool execution.
• Apply PII protection, secure SDLC practices, and audit logging.
• Establish security testing and responsible AI controls for enterprise deployments.

• *6\. Observability and LLMOps**

• Implement OpenTelemetry tracing, structured logging, metrics, dashboards, and alerts.
• Monitor model performance, token usage, latency, and infrastructure costs.
• Maintain operational runbooks and production\-support documentation.

• *7\. Technical Leadership**

• Lead, mentor, and support AI engineers and software developers.
• Conduct code reviews and architecture reviews.
• Prepare HLDs, LLDs, sequence diagrams, and Architecture Decision Records (ADRs).
• Collaborate with project managers and cross\-functional teams to deliver technical milestones.

• *Required Skills and Qualifications**

• 15\+ years of overall software engineering experience.
• 8\+ years of experience in AI/ML, data engineering, or cloud\-native development.
• 2\+ years of hands\-on experience shipping LLM/Generative AI solutions to production.
• 3\+ years of experience leading engineering teams.
• Strong proficiency in Python, asynchronous programming, FastAPI, Pydantic, and pytest.
• Hands\-on experience with LLMs, RAG, embeddings, LangChain, LangGraph, MCP, and agentic workflows.
• Experience with AWS services, including Amazon Bedrock and/or SageMaker AI.
• Knowledge of Docker, Kubernetes, microservices, Kafka, PostgreSQL, Redis, and CI/CD.
• Experience with automated testing, LLM evaluation, production monitoring, and application security.
• Strong problem\-solving, technical documentation, communication, and leadership skills.

• *Preferred Qualifications**

• Experience delivering AI solutions in banking or other regulated industries.
• Exposure to document intelligence, credit, lending, or onboarding workflows.
• Familiarity with Terraform, GitHub Actions, Azure DevOps, SonarQube, and security scanning.
• Knowledge of React, TypeScript, or multilingual AI applications.
• Relevant certifications such as Azure AI Engineer, Azure Solutions Architect, CKA, or CKAD.

• *Education**

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.

Work Location: In person