Solution Architect - Artificial Intelligence/Machine Learning

Description

Job Responsibilities Design & Architecture

• Design end-to-end AI/ML solution architectures that include data ingestion, model development, deployment, and integration with enterprise systems.
• Evaluate business requirements and recommend scalable, secure, and cost-effective architectures across cloud and on-prem environments.
• Develop reference architectures, best practices, and frameworks for AI/ML deployments.
• Integrate AI services and APIs into existing digital platforms and applications.
  

Hands-on Development & Implementation

• Build and fine-tune machine learning and deep learning models (e.g., NLP, computer vision, predictive analytics).
• Implement data pipelines, feature stores, and model serving layers using MLOps best practices.
• Use modern tools and frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, and LangChain (for GenAI use cases).
  

Technical Leadership

• Lead cross-functional teams of engineers, data scientists, and analysts throughout project lifecycles.
• Review architecture designs, code, and infrastructure setups to ensure alignment with best practices.
• Provide mentorship and guidance on AI/ML solution development and architecture principles.
  

Collaboration & Stakeholder Engagement

• Partner with product managers, business analysts, and executives to identify opportunities for AI/ML adoption.
• Translate complex technical concepts into clear business outcomes and ROI discussions.
• Present technical proposals, architecture diagrams, and solution roadmaps to technical and non-technical stakeholders.
  

Required Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, Engineering, or related field.
• 7+ years of experience in software or data solution architecture.
• 3+ years of hands-on experience building and deploying ML/AI models in production.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Experience with cloud-based AI/ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
• Strong understanding of data engineering concepts, including ETL, data lakes, data governance, and
  

• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, DVC, CI/CD pipelines).
• Knowledge of microservices, APIs, and event-driven architectures.
  

Preferred Qualifications

• Experience with Generative AI (LLMs, embeddings, prompt engineering, vector databases).
• Certifications such as AWS Certified Solutions Architect, Azure Solutions Architect Expert, or Google Professional Cloud Architect.
• Experience in one or more industries such as financial services, healthcare, manufacturing, or retail.
• Understanding of AI ethics, data privacy, and model governance frameworks.
• Experience leading AI modernization or digital transformation initiatives.