Own production ML data pipelines from raw sources through feature and training tables using Spark and Databricks. Onboard internal, external, partner, and web data; integrate LLM processing; maintain training-serving consistency; and monitor data quality, drift, and anomalies. Deploy reliable, cost-efficient pipelines through GitHub Actions, Docker, and AWS. Review AI-generated code for correctness, edge cases, and data integrity while collaborating across backend and ML platform systems.
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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