We're looking for a talented AI Data Science Engineer to join our innovative team. You'll be at the forefront of AI technology, designing and implementing custom deep learning models for complex datasets. Your expertise in CNNs, RNNs, and Transformers will be crucial as you tackle challenging problems and turn data into scalable solutions.

Role Overview

The Lead Machine Learning & Data Architecture Engineer is responsible for designing, developing, and maintaining advanced machine learning solutions and robust data architectures. This role leverages deep expertise in AI, data science, and backend engineering to solve complex business problems, architect scalable data systems, and ensure the seamless integration of data-driven applications. The ideal candidate thrives in dynamic, innovative environments, excels in hands-on technical challenges, and is passionate about both individual contribution and collaborative problem-solving.

Key Responsibilities

Architect and implement end-to-end data pipelines and data architectures to support machine learning and analytics solutions, ensuring scalability, reliability, and security.

Lead the development and deployment of advanced machine learning models, including retrieval-augmented generation (RAG), causal inference, predictive modeling, and graph-based algorithms.

Design and maintain data storage solutions (e.g., vector databases, relational and NoSQL databases) optimized for ML workloads and real-time analytics.

Develop and maintain no-code or low-code platforms for data ingestion, cleaning, exploratory analysis, and automated model training.

Collaborate with cross-functional teams, including network operations, product, and engineering, to translate business requirements into technical solutions.

Integrate cloud services (e.g., Azure AI, Databricks, Azure OpenAI) and modern frameworks (e.g., Langchain, LangGraph, FastAPI, Docker) into data and ML pipelines.

Oversee data governance, quality, and lineage, ensuring compliance with best practices and regulatory requirements.

Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.

Proactively identify opportunities for process automation, system optimization, and innovative data-driven solutions.

Stay current with emerging technologies in machine learning, data engineering, and data architecture, recommending and implementing relevant advancements.

Required Experience & Skills

5+ years of experience in machine learning engineering, data science, or backend/data engineering roles.

Proven expertise in Python, SQL, and data science libraries (NumPy, pandas, Jupyter Notebook).

Hands-on experience architecting and deploying ML solutions in cloud environments.

Strong background in building and optimizing data pipelines, data models, and storage solutions for analytics and ML.

Experience with vector databases, graph databases, and time series analysis.

Proficiency in ML frameworks and libraries (e.g., scikit-learn, Dowhy, Langchain, LangGraph).

Demonstrated ability to develop no-code/low-code ML platforms and interactive data applications.

Solid understanding of backend engineering principles, API development (FastAPI), and containerization (Docker).

Experience with DevOps practices, CI/CD, and cloud-based deployment.

Excellent problem-solving skills with a passion for tackling complex, technical challenges in dynamic environments.

Strong communication skills and ability to collaborate across technical and non-technical teams.

Preferred Qualifications

Masters degree in Engineering, Computer Science, or a related field, with a specialization in AI, Applied Mathematics, or Data Science.

Experience in telecommunications, utilities, or high-performance computing domains.

Familiarity with digital twin modeling, causal inference, and graph-based analytics.

Next Steps

Before we engage any further, please complete this self-assessment questionnaire.

https://hr.reisiger.io/?questionnaire=ml-ai