Machine Learning Engineer

What You Will Build

Machine Learning Engineering

• Perform feature engineering, experiment design, model tuning, and offline and online validation.
• Integrate models into scalable product architectures through batch, real-time, or streaming pipelines.
• Improve LLM workflows with prompt design, retrieval-augmented generation, vector search, guardrails, and response quality evaluation.

Production AI Operations

• Strengthen AI systems through CI/CD, monitoring, observability, testing, and model lifecycle automation.
• Monitor production systems for drift, latency, accuracy, cost, bias, reliability, and business impact.
• Debug failures and ensure AI solutions are secure, cost-aware, reliable, and ready for enterprise scale.

Technical Qualifications

• Strong Python programming skills and working knowledge of Java or Go for production services and APIs.
• Solid machine learning fundamentals covering supervised and unsupervised methods such as classification, regression, clustering, ranking, and recommendation.
• Hands-on experience with PyTorch or TensorFlow for model training, fine-tuning, and inference.
• Experience with data preparation, feature engineering, data validation, model evaluation, and production ML deployment.
• Familiarity with generative AI, LLMs, embeddings, vector databases, prompt engineering, and retrieval-augmented generation.
• Working knowledge of MLOps and data infrastructure such as experiment tracking, model versioning, CI/CD, Spark, Kafka, Airflow, and feature stores.

About You

• You have 1–3+ years of experience in machine learning engineering, software engineering, or a related field, with a record of deploying models into production.
• You take ownership of end-to-end solutions and balance experimentation with engineering rigor.
• You are a collaborative problem-solver who can work through ambiguity and deliver measurable business impact.

Team and Environment

Workplace Information

• The description identifies regional customer coverage but does not explicitly state whether this role is onsite, hybrid, or remote.

Employment and Compliance