Senior Machine Learning Engineer (GenAI & Production ML Systems)

Holisticon Insight is a data-focused technology and consulting company, part of the Nexer Group. They build data platforms and data-intensive systems that solve real business problems, working end-to-end from data integration to analytics and AI solutions used in production. Their projects combine data engineering, cloud, and software development, including systems that generate data such as IoT and digital platforms. The company values strong engineering skills, autonomy, and collaboration, maintaining a flat structure and small teams.

• 5+ years of experience in Machine Learning Engineering with production-grade AI systems.
• Expert-level Python skills and strong knowledge of ML libraries such as PyTorch, TensorFlow, scikit-learn, Pandas, and NumPy.
• Hands-on experience with transformer architectures and LLMs (e.g. GPT, BERT, Llama).
• Experience building NLP and Generative AI solutions using frameworks such as Hugging Face and LangChain.
• Practical experience implementing RAG (Retrieval-Augmented Generation) architectures and semantic search systems.
• Experience with prompt engineering techniques and LLM optimization strategies.
• Strong understanding of predictive modeling, anomaly detection, and forecasting techniques.
• Experience deploying and maintaining ML systems in production environments.
• Practical knowledge of MLOps practices, including Docker, Kubernetes, MLflow, and automated ML pipelines.
• Experience designing data preprocessing pipelines for structured and unstructured data sources.
• Strong SQL and data processing skills.
• Solid understanding of model performance optimization, monitoring, and retraining strategies.
• Strong communication and collaboration skills.

Nice to have:

• Experience working in supply chain, engineering, defense, or other regulated industries.
• Experience with secure or on-premise AI deployment environments.
• Familiarity with optimization algorithms and decision-support systems.
• Experience working in Agile delivery models and structured engineering environments.
• Background in building explainable and auditable AI systems.

• Design and develop production-grade Machine Learning and Generative AI solutions.
• Build and optimize NLP and LLM pipelines for document processing and requirements extraction.
• Develop RAG (Retrieval-Augmented Generation) systems and semantic search solutions.
• Create predictive models for forecasting, anomaly detection, and risk scoring.
• Implement prompt engineering strategies to improve LLM performance on domain-specific tasks.
• Design and maintain automated ML pipelines and model deployment workflows.
• Deploy and monitor ML models in secure production environments.
• Optimize model inference performance and scalability.
• Build preprocessing pipelines for both structured and unstructured data sources.
• Collaborate with Data Engineers, Data Scientists, Backend Engineers, and domain experts to deliver end-to-end AI solutions.
• Contribute to architectural decisions, engineering standards, and best practices across AI initiatives.