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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Skills Required
- 5+ years of experience in data engineering, MLOps, or ML platform roles with production pipelines
- Production experience with Spark, including complex transformations, window functions, large joins, partitioning, and tuning for shuffles and skew
- Production Snowflake experience, including SQL, loading, unloading, and cost management
- Experience handling hundreds of millions of rows monthly with incremental loads and backfills
- Strong Python skills, including tested code, Spark UDFs, pandas UDFs, pandas, and PyArrow
- Experience with data quality checks, drift detection, anomaly detection, and actionable alerting
- Experience with GitHub Actions, Docker, AWS S3, IAM, and secrets handling
- Backend engineering experience in service codebases beyond notebooks and SQL
- Daily use of coding agents and an AI-first automation mindset
- Fluent English and residence in Georgia
- Experience with MLflow and feature stores
- Experience with Airflow or Databricks Workflows
- Experience with Kafka or Spark Structured Streaming
- Experience with Terraform
- Experience with dbt
- Scala experience for Spark
- Ability to read Java
- Knowledge of time-based splits and preventing data leakage in training sets
- Ad-tech or e-commerce data experience
