Machine Learning Engineer - Full Stack ML Pipelines

Role Overview

We are seeking an experienced Machine Learning Engineer to design, build, and deploy end-to-end ML pipelines across multi-cloud environments. This role sits at the intersection of data engineering, machine learning, and software development — requiring a rare blend of deep ML expertise and production-grade engineering skills. You will own the full lifecycle of ML systems, from data ingestion and feature engineering through model training, deployment, and monitoring at scale.

What you will do

In this role, you will:

• Architect and implement end-to-end machine learning pipelines spanning data collection, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
• Design and deploy ML workloads across AWS (SageMaker, Lambda, EMR), Google Cloud Platform (Vertex AI, BigQuery ML, Dataflow), and Microsoft Azure (Azure ML, Databricks, Synapse Analytics).
• Build and optimize models using a broad range of methodologies including transformer-based architectures (BERT, GPT, RoBERTa), classical NLP techniques, gradient boosting frameworks (XGBoost, LightGBM, CatBoost), deep learning (CNNs, RNNs, LSTMs), and ensemble methods.
• Develop NLP solutions for text classification, named entity recognition, sentiment analysis, semantic search, summarization, and question answering.
• Implement robust feature stores, data versioning, and experiment tracking using tools such as MLflow, Weights & Biases, DVC, and Feature Store platforms.
• Build scalable data pipelines using Apache Spark, Apache Kafka, Apache Airflow, and cloud-native orchestration tools.
• Containerize and orchestrate ML services using Docker, Kubernetes, and serverless architectures for high-availability inference endpoints.
• Establish CI/CD pipelines for ML (MLOps) to automate model retraining, validation, A/B testing, and canary deployments.
• Monitor model performance in production, detect data drift and concept drift, and implement automated retraining triggers.
• Collaborate with data scientists, product managers, and software engineers to translate business requirements into scalable ML solutions.
• Maintain thorough documentation, conduct code reviews, and contribute to internal ML best practices and standards.

 

What you will bring

The top candidate will have the following skills:

• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field. PhD is a plus.
• Experience: 5+ years of professional experience building and deploying ML models in production environments.
• Programming: Advanced proficiency in Python; strong familiarity with Java, Scala, or Go is a plus.
• ML Frameworks: Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, XGBoost, LightGBM, and spaCy.
• NLP Expertise: Demonstrated experience fine-tuning transformer models (BERT, DistilBERT, GPT variants), building NLP pipelines, and working with text embeddings and vector databases.
• Cloud Platforms: Production experience with at least two of AWS, GCP, and Azure, including their respective ML and data services.
• Data Engineering: Proficiency with SQL, Spark, and distributed data processing frameworks; experience with both batch and real-time streaming pipelines.
• MLOps & Infrast