Machine Learning Engineer
Summary
We are seeking a Machine Learning Engineer to design, develop, deploy, and optimize machine learning solutions that power data-driven products and business decisions. The ideal candidate will have experience building end-to-end ML pipelines, training and fine-tuning models, and deploying scalable solutions in cloud environments.
Key Responsibilities
Design, build, and deploy machine learning models for production applications.
Develop and maintain scalable data pipelines for model training, validation, and inference.
Collaborate with data scientists, software engineers, and business stakeholders to translate requirements into ML solutions.
Fine-tune, optimize, and evaluate machine learning and deep learning models.
Implement feature engineering, model monitoring, and performance tracking processes.
Build and maintain MLOps workflows for model deployment, versioning, and retraining.
Work with structured and unstructured data to improve model accuracy and business outcomes.
Perform experimentation, A/B testing, and model validation.
Ensure solutions meet scalability, reliability, security, and performance requirements.
Stay current with advancements in machine learning, AI, and cloud technologies.
Required Skills
Strong programming experience in Python.
Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
Strong understanding of supervised and unsupervised learning techniques.
Experience with data preprocessing, feature engineering, and model evaluation.
Knowledge of SQL and working with large-scale datasets.
Experience deploying ML solutions in cloud environments such as AWS, Azure, or GCP.
Familiarity with Docker, Kubernetes, and CI/CD pipelines.
Understanding of software engineering best practices and version control systems (Git).
Strong problem-solving and analytical skills.
Preferred Qualifications
Experience with LLMs, Generative AI, RAG, Agents, or NLP applications.
Hands-on experience with model fine-tuning techniques such as LoRA, PEFT, or RLHF.
Experience building and maintaining MLOps platforms.
Knowledge of distributed training and large-scale ML systems.
Experience with vector databases, embeddings, and semantic search.
Cloud certifications are a plus.
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