AI/ML Engineer (Remote)
BigStep Technologies
Date: 3 weeks ago
City: Remote, Remote
Contract type: Full time
Remote
About The Role
We’re looking for top-tier AI/ML Engineers with 6+ years of experience to join our fast-paced and innovative team. If you thrive at the intersection of GenAI, Machine Learning, MLOps, and application development, we want to hear from you. You’ll have the opportunity to work on high-impact GenAI applications and build scalable systems that solve real business problems.
Key Responsibilities
We’re looking for top-tier AI/ML Engineers with 6+ years of experience to join our fast-paced and innovative team. If you thrive at the intersection of GenAI, Machine Learning, MLOps, and application development, we want to hear from you. You’ll have the opportunity to work on high-impact GenAI applications and build scalable systems that solve real business problems.
Key Responsibilities
- Design, develop, and deploy GenAI applications using techniques like RAG (Retrieval Augmented Generation), prompt engineering, model evaluation, and LLM integration.
- Architect and build production-grade Python applications using frameworks such as FastAPI or Flask.
- Implement gRPC services, event-driven systems (Kafka, PubSub), and CI/CD pipelines for scalable deployment.
- Collaborate with cross-functional teams to frame business problems as ML use-cases — regression, classification, ranking, forecasting, and anomaly detection.
- Own end-to-end ML pipeline development: data preprocessing, feature engineering, model training/inference, deployment, and monitoring.
- Work with tools such as Airflow, Dagster, SageMaker, and MLflow to operationalize and orchestrate pipelines.
- Ensure model evaluation, A/B testing, and hyperparameter tuning is done rigorously for production systems.
- Hands-on experience with GenAI/LLM-based applications – RAG, Evals, vector stores, embeddings.
- Strong backend engineering using Python, FastAPI/Flask, gRPC, and event-driven architectures.
- Experience with CI/CD, infrastructure, containerization, and cloud deployment (AWS, GCP, or Azure).
- Proficient in ML best practices: feature selection, hyperparameter tuning, A/B testing, model explainability.
- Proven experience in batch data pipelines and training/inference orchestration.
- Familiarity with tools like Airflow/Dagster, SageMaker, and data pipeline architecture.
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