Technology Lead – GenAI at NorthBay Solutions

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

11 Apr, 26

Salary

0.0

Posted On

11 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI Leadership, AI/ML Foundation, Enterprise Architecture, Hybrid Deployments, Python, TensorFlow, PyTorch, Kubernetes, SQL, NoSQL, APIs, Microservices, DevOps, AWS, Azure, GCP

Industry

IT Services and IT Consulting

Description

Job Title: Technology Lead – AI/ML & GenAI Experience: 12+ Years Location: Abu Dhabi, UAE Employment Type: Full-time About the Role NorthBay Solutions is seeking an accomplished Technology Lead with a strong track record in Generative AI (GenAI), AI/ML, and enterprise architecture. This role is ideal for a senior professional who can design and deliver AI-powered enterprise solutions, lead multidisciplinary teams, and drive innovation across cloud, on-premises, and hybrid environments. You will be the technical authority for GenAI-driven initiatives, ensuring successful delivery of scalable and production-ready AI/ML applications while aligning with enterprise business needs. Core Responsibilities Architect and deliver end-to-end AI/ML and GenAI solutions integrating with existing enterprise systems. Lead a team of 8–12 engineers across AI/ML, full-stack, and DevOps disciplines in Agile environments. Drive technical decision-making across infrastructure, APIs, microservices, databases, and AI platforms. Ensure successful deployment and scaling of AI solutions across on-premises, cloud, and hybrid architectures. Act as a trusted advisor to business stakeholders, bridging technical depth with strategic vision. Technical Expertise Required Generative AI Leadership (3+ Years) – PRIMARY Expert-level experience with LLMs (LLaMA, Mistral, GPT, etc.), including fine-tuning and deployment. Strong hands-on work with Agentic AI, multi-agent systems, and RAG implementations. Proficiency with vector databases, LangChain, LangGraph, and modern GenAI toolchains. Expertise in advanced prompt engineering and Chain-of-Thought techniques. Proven track record delivering 3+ production-grade GenAI applications. AI/ML Foundation (4–5 Years) Hands-on experience with Python, TensorFlow, and PyTorch for building production ML systems. Full lifecycle management of ML models, from development to monitoring in production. Seamless integration of AI/ML models into enterprise-grade applications and platforms. Enterprise Architecture & Deployment (8+ Years) Strong background in full-stack development (MERN stack or equivalent). Deployment expertise across on-premises, cloud, and hybrid environments. Advanced Kubernetes skills for orchestration and scaling. Database expertise in SQL (PostgreSQL/MySQL) and NoSQL (MongoDB/DynamoDB). Proven ability to design and manage APIs and microservices (REST, GraphQL). DevOps proficiency with Docker, CI/CD pipelines, and infrastructure automation. Cloud & Infrastructure Deep knowledge of AWS, Azure, or GCP with ML/AI services. AWS expertise highly valued: SageMaker, Bedrock, Lambda, API Gateway. Strong grasp of hybrid cloud architectures integrating AI/ML workloads. Proven Delivery 7+ enterprise-scale projects successfully delivered in AI/ML and enterprise systems. Demonstrated team leadership experience with measurable outcomes. Strong focus on delivery excellence, scalability, and long-term maintainability. Requirements (Preferred) Certifications in AWS/Azure/GCP (Solutions Architect, ML Specialty). Excellent communication and stakeholder management skills. Solid experience in Agile/Scrum leadership, driving productivity and team performance. Ideal Candidate Profile Priority expertise: Generative AI Leadership AI/ML Foundation Enterprise Architecture & Hybrid Deployments A strategic leader with hands-on depth, able to innovate, mentor, and deliver at scale.

Responsibilities

The Technology Lead will architect and deliver AI/ML and GenAI solutions while leading a team of engineers. They will ensure successful deployment and scaling of AI solutions across various architectures.