Senior Machine Learning Engineer

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our team. This role is ideal for someone with strong expertise in designing, deploying, and managing advanced ML systems with a focus on automation and reliability. You’ll play a key role in shaping the tools, practices, and standards used across the ML lifecycle, collaborating closely with both technical and non-technical stakeholders.

Responsibilities:

• Lead the design, development, and operational lifecycle of Machine Learning pipelines, incorporating industry best practices.

• Design, implement, and enhance critical steps in ML pipelines for scalability, efficiency, and reliability.

• Own and drive the design and execution of ML prediction endpoints, ensuring robust performance and alignment with project requirements.

• Collaborate with Systems Engineers to create a reliable ML lifecycle management environment, integrating seamlessly with existing systems.

• Draft technical specifications, documentation, and user guides to support developed applications and workflows.

• Advocate for and support improvements in coding practices and organization within scientific work cycle repositories.

• Build and manage ML pipelines across various projects, ensuring rapid deployment and iterative improvements.

• Identify and assess technical risks and gaps, proposing and implementing risk mitigation strategies.

• Partner with Data Scientists to transition predictive models into production environments, creating scalable data preparation pipelines and understanding model scopes and objectives.

Requirements:

• Bachelor’s Degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience).

• Experience: Minimum of 3 years in a Machine Learning Engineer role or a similar position.

• Programming Skills: Strong proficiency in Python and related ML libraries (e.g., PyTorch, Hugging Face, Transformers).

• Data Science & ML Knowledge: Solid grasp of Data Science principles and Machine Learning fundamentals, with hands-on model creation experience. Strong background in machine learning algorithms, including creating models from scratch.

• Engineering Best Practices: Extensive experience in implementing engineering best practices and a deep understanding of Machine Learning fundamentals.

• Data Products & Big Data Technologies: Hands-on experience with technologies like Apache Spark (Spark SQL, MLlib/Spark ML) or similar big data frameworks, and proficiency in tools such as Hadoop, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, Apache Flink, etc.

• Automation & Workflow Management: Experience with automated data pipeline and workflow tools, such as Airflow, Argo Workflows, Kubeflow, etc.

• Data Processing Paradigms: Familiarity with batch, micro-batch, and streaming data processing paradigms.

• Cloud Providers: Practical experience with major cloud providers, including AWS, GCP, or Azure.

• MLOps Platforms & Tools: Proficiency in one or more MLOps platforms/technologies such as AWS SageMaker, Azure ML, GCP Vertex AI, Databricks, MLFlow, Kubeflow, or TensorFlow Extended (TFX).

• Production ML Integration: Experience in integrating ML models within complex, data-driven systems in production environments.

• Language Proficiency: Fluent in English, B2+ level or higher.

Preferred Qualifications:

• ML Tools for Advanced Use Cases: Experience with Large Language Models (LLMs) and computer vision applications, including image generation tools, Speech-to-Text (STT), Speech-to-Speech (STS), and Text-to-Speech (TTS).

• Certifications: AWS, GCP, or Azure certifications are a strong advantage.

• Adaptability: Ability to quickly adapt to new technologies and environments, with a startup mindset for handling ambiguity and fast-paced change.

If you’re passionate about Machine Learning and eager to work in a dynamic, innovative environment, we encourage you to apply!