SaaS e-commerce Client #3 | Senior MLOps Engineer (Data & ML Platform)

On behalf of NDA, SaaS e-commerce, SD Solutions is looking for a talented Senior MLOps Engineer (Data & ML Platform) based in Georgia (Tbilisi) to join our growing team.

You’ll own the data that Falcon’s offer-ranking model learns from: the raw sources, the pipelines, the training tables, and the monitoring around them.

SD Solutions is a staffing company operating globally. Contact us to get more details about the benefits we offer.

Responsibilities:

What you’ll do:

• Own the ML data pipelines: build and run batch pipelines on Spark and Databricks, from raw data to feature and training tables, including enrichment and backfills for late-arriving data.
• Onboard new data sources: bring in internal databases, external APIs, partner feeds and web data, and match them to Falcon’s own entities.
• Use LLMs inside pipelines: add LLM steps to batch jobs, such as turning text into embeddings and structured attributes.
• Keep training and serving consistent: make sure features match between training and the live inference service, which includes reading its Java code.
• Monitor data and model inputs: run scheduled data quality checks and Grafana alerts that catch bad data before it reaches the model, and find the root cause when something breaks.
• Ship like an MLOps engineer: deploy pipelines through CI/CD (GitHub Actions, Docker, AWS) with proper secrets handling, and keep run time and cost under control.
• Work with AI agents: coding agents write most of Falcon’s code. You’ll decide where agents fit, then check their output on row counts, distributions and edge cases before anyone relies on it.

Requirements:

• 5+ years in data engineering, MLOps or ML platform roles, with pipelines you’ve run in production
• Production Spark: complex transformations, window functions, large joins, partitioning, and tuning for shuffles and skew. Databricks (jobs, Delta tables) is a big plus.
• Production Snowflake: SQL, loading and unloading, and cost management
• Large data volumes: hundreds of millions of rows a month, with incremental loads and backfills that keep run time and cost under control
• Strong Python: clean, tested code; Spark UDFs including pandas UDFs; pandas and PyArrow
• Data quality and monitoring: quality checks, drift and anomaly detection, and alerts people can act on
• Deployment: GitHub Actions, Docker, AWS (S3, IAM) and proper secrets handling
• Backend engineering: comfortable working in a service codebase, not only in notebooks and SQL
• AI-first habits: you already use coding agents daily and look for what to automate
• Fluent English and based in Georgia

Nice to have:

• MLflow and feature stores
• Airflow or Databricks Workflows
• Kafka or Spark Structured Streaming
• Terraform
• DBT
• Scala for Spark
• Reading Java
• Time-based splits and preventing data leakage in training sets
• Ad-tech or e-commerce data experience

About the company:

NDA helps eCommerce brands boost revenue with highly relevant and rewarding post-purchase offers. Our platform delivers personalized offers to customers right after checkout, driving repeat purchases and increasing lifetime value through seamless integrations and data-driven insights

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