About the role

You will join a team of talented and friendly Data Scientists in Machine Learning Operation (MLOPs) and AI (Artificial Intelligence) as a Data Science Squad member.

Team develops state-of-the-art Machine Learning-powered services for automated Supply Chain optimisation, Pricing strategy improvements, and Service Lifecycle Management including Generative AI-powered Knowledge management and warranty claim fraud detection and more.

Team is performing full MLOps cycles from customer pain discovery to production.

What would you do?

• As an MLOps Data Engineer, you will be responsible for building high quality ML datasets at scale, used to train ML models that power AI-enabled solutions.
• To build foundational tools and data pipelines to ingest, normalize and clean the valuable data that would be fundamental for our Data Scientists and ML engineers in Syncron
• You will be responsible for establishing and executing the strategy for our organization’s ML Data Pipelines, with an initial focus on agile ML Data Ops
• Identification of infrastructure components and data stack to be used, design and implementation of pipelines between data systems and teams, automation workflows, data enrichment and monitoring tools all for AI models
• Code and contribute to the stack.
• Dive into our dataset and design, implement and scale data pre/post processing pipelines of ML models,
• Work on applied ML solutions in the areas of data mining, cleaning, normalizing and modelling.
• Build data processing streams for cleaning and modelling text data for LLMs,
• Work with Privacy and Security team on data governance, risk and compliance initiatives,
• Work on initiatives to ensure stability, performance and reliability of our data infrastructure.

Who you are?

• An exceptional data engineer who is passionate about data for AI and values it can bring to Syncron, who loves working with data ops at scale; and who is committed to the hard work necessary to continuously improve our ML data pipelines.
• Bachelors in Computer Science, Mathematics, Physics, or a related fields.
• Experience in statistical analysis & visualization on datasets using Pandas
• Experience designing and building highly available, distributed systems of data extraction, ingestion, normalization and processing of large data sets in real time as well as batch, that will be used across engineering teams using orchestration frameworks like Airflow, KubeFlow or other pipeline tools,
• Demonstrated prior experience in creating data pipelines for text data sets NLP large language models and generative AI.
• Ability to produce well-engineered software, including appropriate automated test suites, technical documentation, and operational data strategy.
• Excellent coding skills in Python, Java, bash, SQL, and expertise with Git version control.
• Experience using big data technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark),
• Experience with any public cloud environment – AWS
• Significant experience with relational databases and query authoring (SQL) as well as NoSQL databases like DynamoDB etc.,
• Experience building and maintaining ETL (managing high-quality reliable ETL pipelines).