Machine Learning Architect

About the Role:

Machine Learning Architect

Key Responsibilities:

Architecture & Design

• Design and own end-to-end

  machine learning system architectures

  , from data ingestion to model deployment and monitoring.
• Define

  ML infrastructure standards

  , frameworks, and best practices for scalable model development.
• Collaborate with Data Engineers, MLOps Engineers, and Data Scientists to ensure seamless integration of ML models into production environments.
  
• Architect

  real-time and batch inference systems

  using modern ML frameworks and cloud technologies.
• Establish

  governance frameworks

  for model lifecycle management, data versioning, and reproducibility.

Development & Deployment

• Lead the

  design, development, and optimization

  of machine learning models and pipelines.
• Implement

  CI/CD and MLOps practices

  for continuous integration, model retraining, and automated deployment.
• Optimize model performance, scalability, and cost-effectiveness on cloud platforms (AWS, Azure, GCP).
  
• Ensure robust

  data preprocessing, feature engineering

  , and

  feature store design

  .

Monitoring & Governance

• Implement

  model monitoring, drift detection

  , and

  automated retraining

  pipelines.
• Ensure compliance with

  data privacy, security, and ethical AI guidelines

  .
• Collaborate with stakeholders to define and maintain documentation, audit trails, and explainability standards.
  

Preferred Skills:

• Experience designing

  LLM (Large Language Model)

  architectures and fine-tuning foundation models.
• Knowledge of

  vector databases

  (Pinecone, Weaviate, FAISS) and

  retrieval-augmented generation (RAG)

  pipelines.
• Familiarity with

  edge AI

  ,

  real-time inference

  , and

  streaming data

  architectures (Kafka, Flink).