AI Engineering Manager

Role Summary

Hands-on technical leader managing the AI Engineering team within the client’s AI Factory. Responsible for driving the full AI model development lifecycle end-to-end,  from data curation and pre-training to production deployment and monitoring.

Works closely with or reports directly to the Chief AI Officer and plays a key role in defining the AI/ML strategy, architecture, and execution standards across the organization.

Key Responsibilities

• Lead, mentor, and inspire a team of AI/ML and MLOps engineers building production-grade AI solutions across customer analytics, revenue optimization, operational efficiency.

• Own the end-to-end AI/ML lifecycle: data preparation, model training, evaluation, deployment, monitoring and continuous improvement.

• Drive development of foundation models and domain-specific LLM/SLM solutions for telecom use cases.

• Oversee adoption of advanced MLOps practices, including CI/CD pipelines, model versioning, automated retraining and drift monitoring.

• Lead implementation of hybrid AI infrastructure (cloud + on-prem inference) and scalable GPU-based training environments.

• Collaborate closely with Data Engineering, BI and infrastructure teams to integrate AI systems into enterprise platforms.

• Ensure alignment with enterprise AI governance, security and responsible AI standards.

Must-Have Skills

AI/ML Engineering Leadership

• 7+ years in AI/ML engineering, including 3+ years in a leadership role
• Experience managing teams of 5-15 AI/ML engineers
• Proven track record of deploying AI/ML models to production at scale (not POCs)
• Experience in telecom, enterprise, or regulated environments

NVIDIA / LLM Ecosystem

• NVIDIA NeMo (training, fine-tuning, alignment) and NeMo Curator (data curation, dataset preparation)
• Triton Inference Server or TensorRT for model serving
• Experience with NVIDIA GPU clusters (A100/H100) and distributed training
• Understanding of NVIDIA AI Enterprise stack

LLM / Foundation Models

• Pre-training or large-scale adaptation of LLMs/SLMs (not only fine-tuning)
• RLHF, DPO, prompt engineering, and synthetic data generation
• Custom tokenization for domain-specific or low-resource languages
• Experience with ASR and TTS systems

MLOps / Infrastructure

• MLOps pipelines (Airflow, Kubeflow or similar) and LLMOps practices
• Kubernetes-based ML workloads (training and inference)
• AWS SageMaker (incl. HyperPod, Spot Training)
• Hybrid cloud/on-prem deployment architectures

Nice-to-Have

• Experience with enterprise MLOps platforms (MLflow, Weights & Biases, Databricks, Vertex AI, SageMaker Studio)
• AI governance and responsible AI frameworks
• Open-source NLP/AI contributions or research publications
• Experience with Cloudera / CDP ecosystem

Skills and Competencies

• Strong hands-on technical leadership with ability to go deep into architecture and implementation details
• Strategic mindset with ability to translate business needs into scalable AI systems
• Strong understanding of production AI systems (latency, scalability, cost, reliability trade-offs)
• Excellent stakeholder management and cross-functional communication skills
• Passion for building advanced AI systems and driving AI transformation at enterprise scale