Machine Learning Systems Developer

2 weeks ago


vijayawada, India beBeeMachine Full time

Machine Learning EngineerAdZeta is turning data insights into profit signals by integrating seamlessly with the ad stack. We unify first-party data, predict lifetime value, activate high-value signals, and prove revenue impact—governed, auditable, and real-time.This role requires a skilled engineer to build and own the end-to-end ML pipeline, from raw data to features, models, deployment, monitoring, and activation. The ideal candidate should have experience shipping DS/ML systems to production and be proficient in Python, SQL, and cloud technologies.Data plumbing: Ingest and model data across various sources; define event taxonomy, identity graph, and data contracts.Feature engineering: Build offline/online features for pLTV, propensity, and clustering; ensure parity with a feature store and model registry.ML pipeline & training: Stand up reproducible training pipelines with experiment tracking, hyperparameter search, and evaluation.MLOps & deployment: Package models, deploy to managed services, wire CI/CD, and implement blue-green/rollback patterns.Orchestration: Schedule jobs with Airflow/Prefect; manage dependencies, SLAs, retries, and design backfills and incremental loads.This position offers meaningful equity and the chance to build the ML backbone of a category-defining product. If you're a seasoned engineer looking for a challenging opportunity, apply with your best 0→1 ML build and a link to a relevant repo/case study.Key Responsibilities:Design measurement plans and communicate trade-offs with clarity.Thrive in ambiguity, move fast, and own outcomes end-to-end.Partner with product/GTM on uplift tests, holdouts, and calibration to prove incremental value.Requirements:7-10+ years shipping DS/ML systems to production.Fluent in Python, SQL, and one major cloud (GCP/AWS), with BigQuery/Snowflake and dbt experience.MLOps knowledge (MLflow/Weights & Biases, Feast/feature store, Docker/K8s, CI/CD) and orchestration (Airflow/Prefect).Benefits:Founding-team impact and meaningful equity.The chance to build the ML backbone of a category-defining product.



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