ML Engineer- W2

Job Title – Machine Learning Engineer Job Type – Remote Duration- 6+ months Job Description:

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field.
• 8+ years of experience developing machine learning solutions in production environments.
• Strong proficiency in Python and software engineering best practices.
• Experience with machine learning frameworks such as:
• TensorFlow
• PyTorch
• Scikit-learn
• XGBoost
• Strong understanding of supervised and unsupervised learning techniques.
• Experience building and managing data pipelines.
• Knowledge of SQL and large-scale data processing technologies.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Experience with version control systems and CI/CD processes.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Master's degree or PhD in a relevant technical field.
• Experience with large language models (LLMs), generative AI, or retrieval-augmented generation (RAG).
• Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
• Experience with distributed computing platforms such as Spark.
• Familiarity with vector databases and embedding technologies.
• Experience building AI-powered applications in a production environment.
• Knowledge of responsible AI, model governance, and explainability techniques.

Technical Skills Programming

• Python
• SQL
• Java, Scala, or C++ (preferred)

Machine Learning & AI

• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Predictive Modeling
• Generative AI
• LLM Fine-Tuning

Cloud & Infrastructure

• AWS, Azure, or GCP
• Docker
• Kubernetes
• REST APIs
• CI/CD

Data Technologies

• Spark
• Databricks
• Airflow
• Feature Stores
• Data Warehousing Solutions

Success Metrics

• Model accuracy and performance improvements
• Deployment reliability and scalability
• Reduction in model latency and infrastructure costs
• Business impact generated through AI initiatives
• Quality and maintainability of production ML systems
• Operational excellence through effective monitoring and automation