Machine Learning Engineer

ML Engineer

Experience-5+ Years

Immediate Joiners Only

Location : Hyderabad

Job Description:

We are seeking an experienced Machine Learning Engineer to design, build, deploy, and maintain scalable ML systems in a production environment. This role involves close collaboration with data scientists, ownership of ML pipelines, and hands-on work with Google Cloud Platform. The ideal candidate will have strong experience in MLOps, production deployments, monitoring, and ML systems reliability.

Key Responsibilities:

Build and maintain reliable data pipelines

Prepare clean, structured datasets for machine learning

Work with data from clients data warehouse and other internal sources

Use transformation tools like dbt when needed

Automate ML training workflows on Google Cloud Platform

Build reproducible ML pipelines

Work with data scientists to turn experimental models into production-ready systems

Deployment & Serving

Deploy ML models using Cloud Run, Kubernetes, and Vertex AI

Build and maintain REST APIs in Python to serve predictions

Ensure models are fast, stable, and secure

Set up dashboards with DataDog, Grafana, or similar tools

Monitor model performance, accuracy, data drift, and system health

Troubleshoot issues and ensure smooth operation in production

Required Skills and Qualifications:

5+ years of experience building and deploying ML models in production

Strong Python skills (pipelines, training workflows, APIs)

Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)

Solid understanding of MLOps practices: CI/CD, model versioning, automated pipelines, monitoring

Hands-on experience with GCP services: Cloud Run, Kubernetes, BigQuery, Vertex AI

Experience building APIs (FastAPI, Flask, or similar)

Good understanding of automated testing Monitoring: Experience with DataDog or Grafana Knowledge of ethical AI, bias mitigation, or data privacy principles Knowledge of AI guardrailing Experience with recommender systems Vector databases Embeddings Feature engineering Matrix factorization techniques Ttwin tower models Basic experience or exposure to recommender systems Monitoring and observability of models in production User feedback handling Model training and evaluation (at least theoretical understanding) A/B testing Performance metrics