Artificial Intelligence Architect

Responsibilities

• Discuss the feasibility of AI/ML use cases along with architectural design with business teams, and translate the vision of business leaders into a realistic technical implementation.
• Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings.
• Design and implement robust ML infrastructure and deployment pipelines.
• Establish comprehensive MLOps practices for model training, versioning, and deployment.
• Lead the development of HR-specialized language models (SLMs).
• Implement model monitoring, observability, and performance optimization frameworks.
• Develop and execute fine-tuning strategies for large language models.
• Create and maintain data quality assessment and validation processes.
• Design model versioning systems and A/B testing frameworks.
• Define technical standards and best practices for AI development.
• Optimize infrastructure for cost, performance, and scalability.
  

Requirements

• 7+ years of experience in ML/AI engineering or related technical roles.
• 3+ years of hands-on experience with MLOps and production ML systems.
• Demonstrated expertise in fine-tuning and adapting foundation models.
• Strong knowledge of model serving infrastructure and orchestration.
• Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc. )
• Experience implementing model versioning and A/B testing frameworks.
• Strong background in data quality methodologies for ML training.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Experience with cloud-based ML platforms (AWS, Azure, Google Cloud).
• Proven track record of deploying ML models at scale.
• Experience developing AI applications for enterprise software domains.
• Knowledge of distributed training techniques and infrastructure.
• Experience with retrieval-augmented generation (RAG) systems.
• Familiarity with vector databases (Pinecone, Weaviate, Milvus).
• Understanding of responsible AI practices and bias mitigation.
• Bachelor's or Master's degree in Computer Science, Machine Learning, or related field.