We are seeking a high-impact AI/ML Engineering Lead to spearhead our AI transformation. This is a Player-Coach role designed for a technical visionary who can still go deep into the codebase while managing a high-performing team of engineers.

You will lead the integration of Traditional Machine Learning (Predictive Modeling, Classification) with Generative AI (LLMs, Diffusion Models) to build intelligent products. Your goal is to move beyond AI as a feature to AI as the core, ensuring our systems are scalable, ethically governed, and deliver measurable business ROI.

Key Responsibilities

• Team Leadership: Build, mentor, and lead a multidisciplinary team of ML Engineers and Data Scientists. Foster a culture of rapid prototyping and rigorous production standards.

• GenAI Strategy: Architect and deploy Generative AI solutions, including RAG (Retrieval-Augmented Generation) pipelines, fine-tuned LLMs, and autonomous AI agents.

• Production Excellence: Own the end-to-end ML lifecycle (MLOps), ensuring that models are not just accurate but also cost-efficient, low-latency, and resilient.

• Hybrid Modeling: Lead the team in combining traditional discriminative models with generative capabilities to solve complex, multi-modal problems.

• Governance & Ethics: Establish guardrails for AI safety, including bias detection, halluncination mitigation, and data privacy compliance (GDPR/UAE Data Law).

• Cross-functional Alignment: Work directly with CTO/Product VPs to translate business challenges into technical AI roadmaps.

Requirements

• Education: Masters or PhD in Computer Science, Artificial Intelligence, or a related field.

• Experience: 8+ years in Software/ML Engineering, with at least 2+ years in a leadership or managerial capacity.

• GenAI Expertise: Proven experience with LLM frameworks (LangChain, LlamaIndex) and fine-tuning techniques (LoRA, QLoRA).

• Deep Learning Mastery: Expert knowledge of Transformers, CNNs, and RNNs using PyTorch or TensorFlow.

• Programming: Mastery of Python and experience with specialized libraries like Hugging Face, NumPy, and Scikit-learn.

Technical Leadership Stack

• LLM Ops: Experience with Vector Databases (Pinecone, Milvus, Weaviate) and prompt engineering at scale.

• Infrastructure: Kubernetes, Docker, and GPU resource management (NVIDIA/CUDA).

• Cloud Platforms: Advanced proficiency in AWS SageMaker, Google Vertex AI, or Azure AI Studio.

• Monitoring: Weights & Biases, MLflow, or Arize for model observability.

Work Details

• Location: [City/Hybrid/Remote] (e.g., Dubai, Bengaluru, or Global Remote).

• Schedule: Full-time.

• Compensation: Executive-level salary + Performance-based Equity + Leadership Bonuses.

• Benefits: * Personalized Development: Access to executive coaching and global AI summits.

  • Health & Wellness: Premium global health cover for family.

  • Innovation Time: Dedicated 10% time for Moonshot R&D projects.