Tech Stack

Description

In this role, you will design and build mission-critical infrastructure powering the ML lifecycle, from large-scale data processing and feature engineering to model training, deployment, and monitoring. You'll work with modern cloud architectures and AI solutions to drive smarter decision-making.

Requirements

• 5+ years of experience in software, data, or ML engineering
• Strong proficiency in SQL
• Experience with Spark, dbt, Dagster/Airflow, Snowflake/Redshift/BigQuery
• Proficiency in Terraform, Docker, Kubernetes, CI/CD (Jenkins), ML lifecycle (MLflow, Kubeflow, W&B)
• Hands-on AWS experience (S3, EC2, Lambda, RDS, EMR, SageMaker, Bedrock) and Python performance tuning

Responsibilities

• Architect and implement full ML model lifecycle from data ingestion to production inference using gRPC, Kafka, FastAPI, Spring WebFlux, Axum
• Engineer and automate large-scale data processing pipelines with Spark, dbt, and workflow orchestrators; lead Feature Store strategy
• Own MLOps framework for model training, versioning, deployment, CI/CD pipelines, and experiment tracking with MLflow, Kubeflow, W&B
• Implement deployment strategies including canary, blue-green, shadow, A/B testing; optimize inference for LLMs using quantization and compilation
• Define and manage cloud infrastructure with Terraform across compute, storage, and ML platforms on AWS