Senior AI/ML Engineer

Position Overview

Join our cutting-edge team as a Senior AI-ML Engineer, focusing on designing, developing, and deploying advanced machine learning solutions that power data-driven insights and intelligent applications. In this pivotal role, you will harness sophisticated ML frameworks, cloud-native technologies, and distributed computing principles to create scalable and secure AI systems. Collaborate closely with our Senior Data Applications Engineer to ensure seamless data ingestion, pre-processing, and feature engineering, elevating the performance of end-to-end ML pipelines while charting the course for our team’s AI innovation.

Key Responsibilities

Advanced ML System Design

• Architect and deploy scalable machine learning and deep learning models leveraging frameworks such as TensorFlow, PyTorch, or similar libraries.
• Establish effective model management practices, from experiment tracking to packaging and version control.

Data Pipeline and Feature Engineering

• Collaborate with data engineering teams to design robust data pipelines, ensuring data cleanliness and quality for model training and inference.
• Implement advanced feature engineering strategies, including dimensionality reduction, feature selection, and transformation of complex unstructured data.

Distributed Training and Optimization

• Develop and optimize distributed training solutions across CPU, GPU, or multi-node environments, leveraging technologies such as Apache Spark, Ray, or Kubernetes.
• Identify model training bottlenecks, implementing performance optimizations for large-scale data sets.

End-to-End ML Lifecycle Management

• Oversee the entire ML development lifecycle (MLOps), including continuous integration/continuous delivery (CI/CD) of ML models, model deployment, monitoring, and ongoing maintenance.
• Define and implement robust validation, testing, and A/B experimentation strategies to ensure reliability and accuracy of ML models.

Complex System Integration

• Align AI-ML workflows with broader distributed systems and microservices architecture, leveraging containerization (Docker) and orchestration (Kubernetes) best practices.
• Integrate AI-ML models into enterprise applications, ensuring low latency and high availability to support dynamic real-time operations.

Technical Leadership and Collaboration

• Partner with software engineers, data scientists, and product teams to translate business challenges into data-driven AI solutions.
• Mentor junior team members, establishing best practices and driving continuous improvement in AI-ML development processes.

Strategic Innovation

• Stay current on emerging trends in machine learning, deep learning, and data science.
• Propose new AI initiatives that enhance scalability, performance, and reliability, while aligning with organizational goals.

Required Skills and Qualifications

Deep Learning & ML Expertise

• In-depth knowledge of machine learning, deep learning, and related statistical methods.
• Proficiency in Python, R, or Scala, with hands-on experience using ML libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Mathematical & Statistical Foundation

• Strong background in linear algebra, probability theory, optimization, and statistical modeling.
• Experience in advanced modeling techniques such as reinforcement learning, NLP, or computer vision is a plus.

Distributed Systems & Cloud-Native ML

• Demonstrated ability to run large-scale model training using distributed systems (e.g., Spark, Ray) and to deploy models in cloud-native environments .
• Hands-on experience with containerized applications (Docker) and orchestration (Kubernetes), with a focus on ML workloads.

MLOps & CI/CD

• Expertise in designing and operationalizing ML pipelines, including feature stores, model registries, and automated deployment strategies.

Collaboration & Communication

• Strong collaboration skills, with a history of partnering across data engineering, software development, and product teams.
• Effective written and verbal communication, capable of translating complex technical concepts into actionable insights for stakeholders.

Education

• BSc or MSc in Computer Science, Data Science, Electrical Engineering, or a related field. A PhD is a plus.

This senior-level position is ideal for professionals with 5–7+ years of extensive AI-ML development experience, preferably in environments that handle large-scale data, complex modeling needs, and sophisticated distributed systems. Candidates should showcase a record of leading innovative AI projects, delivering end-to-end ML solutions, and driving measurable business impact through advanced analytics and AI methodologies.

Why Join Us?

Our R&D offices in Urla and Istanbul are at the forefront of technology innovation, offering a vibrant work environment where creativity and effective solutions are rewarded. Team members are encouraged to take initiative, lead projects that stir their passion, and enhance productivity through cutting-edge technologies.

*All applications will be treated with strict confidentiality.