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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