Must have

• Python

• Microservices

• Infrastructure as Code

• Terraform

• Cloud platform

• Airflow

• MWAA

• AWS Lambda

• AWS EC2

• Amazon EMR

• ArgoCD

• ETL

• Big Data

• Spark

• Flink

• TensorFlow

• PyTorch

• Kafka

• gRPC

• HTTP

• Prometheus

• Grafana

• AWS

• Redis

• Aerospike Database

• English

Nice to have

• Kubernetes

• OOP

• SQL

Requirements description

• Degree in Computer Science or a related field.
• At least 2 years of industry experience working with microservices.
• Experience with Infrastructure as Code (e.g., Terraform) and cloud platforms (AWS – including Sagemaker, Airflow MWAA, Step/Lambda, EC2, EMR).
• Proficiency in CI/CD tools such as GitHub Actions, ArgoCD.
• Familiarity with ETL workflows, big data processing, and ML frameworks (e.g., Spark, Flink, TensorFlow, PyTorch, Kafka).
• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Strong scripting/programming skills in Python (Go is a plus).
• Familiarity with communication protocols (e.g., gRPC, HTTP/2).
• Experience in real-time monitoring/alerting using Prometheus, Grafana, or AWS Quicksight.
• Knowledge of distributed caching systems (e.g., Redis, Aerospike).

Preferred Qualifications:

• 3+ years of experience with low-latency, high-throughput distributed microservices.
• Experience designing system architecture for ML platforms.
• Familiarity with online testing frameworks (A/B testing, canary, blue-green deployment).
• Experience with ML serving tools (e.g., Seldon, Triton, ONNX, TensorRT).
• Background in AdTech, recommendation systems, or RTB environments.
• Knowledge of additional OOP languages and SQL scripting.
• Familiarity with serialization protocols such as Protobuf, FlatBuffers, Cap’n Proto.

Offer description

Job Title: ML Ops Engineer

We are seeking an experienced ML Ops Engineer to support the development and deployment of ML infrastructure for an advanced advertising platform delivering sponsored content to millions of media-enabled devices worldwide. The role focuses on designing, implementing, and maintaining reliable infrastructure for deploying, monitoring, and managing machine learning models in production environments.

Technologies Used:

• Python, Go
• REST, gRPC
• AWS (Sagemaker, EC2, EMR, Lambda)
• Terraform
• Spark, Hadoop
• Snowflake, Snowpark
• GitHub Actions, ArgoCD
• Airflow
• Kubernetes
• Grafana, Prometheus
• TensorFlow, PyTorch
• Redis, Aerospike

Your responsibilities

1. Design and manage infrastructure for ML model deployment, monitoring, and maintenance.
2. Build and scale microservices to support ML pipelines and real-time operations.
3. Work with cross-functional teams to integrate ML workflows into production systems.
4. Develop and maintain CI/CD pipelines and infrastructure as code using modern tools.
5. Monitor system health and performance using real-time alerting and visualization tools.