AIML Architect WITH MLOPS Experience

AI/ML Architect

As an AI/ML Architect, you will bring deep expertise in model development, system design, and deployment strategies. You will work closely with cross-functional teams to define and implement AI-driven solutions that are both technically sound and aligned with business goals.

Key Responsibilities:

• Architect end-to-end AI/ML solutions using deep learning, NLP/NLU, LLMs, and multimodal models.
• Design and build scalable, production-grade ML systems integrated with backend services, APIs, and enterprise data platforms.
• Evaluate, fine-tune, and deploy large-scale LLMs and Generative AI models for various enterprise use cases.
• Lead technical design and hands-on development of ML pipelines and inference services.
• Collaborate with engineering, data science, and product teams to identify opportunities for AI automation and optimization.
• Troubleshoot model and system performance issues in distributed environments.
• Contribute to the development and enforcement of ML best practices, model governance, and technical standards.
• Present complex ML concepts and technical insights clearly to both technical and non-technical stakeholders.

Required Qualifications:

• 14+ years of experience in designing, building, and deploying AI/ML systems in enterprise environments.
• Strong foundation in machine learning, deep learning, and statistical modeling.
• Proven experience with Generative AI and large language models (text and multimodal).
• Hands-on expertise in Python and ML/NLP frameworks such as PyTorch, TensorFlow, Hugging Face, NumPy, and Pandas.
• Deep experience in natural language processing (NLP), natural language understanding (NLU), and conversational AI systems.
• Demonstrated ability to integrate ML models into scalable backend systems via APIs and data connectors.
• Excellent problem-solving skills, especially in distributed and large-scale system contexts.
• Strong communication and documentation skills; able to work independently as a technical leader within projects.

Preferred Qualifications:

• Experience with MLOps frameworks (e.g., MLflow, Airflow, Kubeflow) and model lifecycle management.
• Knowledge of responsible AI practices, model interpretability, and ethical considerations.
• Experience working with hybrid cloud/on-prem deployment models.
• Familiarity with multimodal AI (e.g., text + image, vision-language models).