AI Engineering Manager

About the Position

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too.

We are seeking an experienced AI Engineering Manager to lead our AI Engineering team, focusing on AI/ML strategy and execution.

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.

Requirements

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 the ability to go deep into architecture and implementation details
• Strategic mindset with the 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

We Offer

• Covered vacation period: 20 business days and 5 days off
• Free English classes
• Flexible working schedule
• Truly friendly and supporting atmosphere
• Working remotely or in one of our offices
• Medical insurance for employees from Ukraine
• Legal support

About the Company

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