The AI Architect is responsible for translating business needs into AI-driven solutions, designing end-to-end systems, collaborating with stakeholders, ensuring governance, and overseeing delivery of AI projects.
- Translate business requirements into AI-driven solution architectures
- Design end-to-end systems including data pipelines, model development, deployment, and monitoring
- Define reference architectures, reusable frameworks, and best practices
2. Stakeholder Engagement
- Collaborate with business leaders, product managers, and engineering teams to identify AI opportunities
- Act as a trusted advisor, explaining AI capabilities, trade-offs, and ROI
- Lead solution workshops, PoCs, and technical discussions
3. Technology Leadership
- Select appropriate tools, platforms, and frameworks (e.g., TensorFlow, PyTorch, cloud AI services)
- Architect solutions leveraging cloud platforms (AWS / Azure / GCP) and modern data stacks
- Ensure scalability, reliability, and performance of AI systems
4. Governance & Responsible AI
- Define and enforce AI governance, ethics, and compliance standards
- Ensure solutions adhere to security, privacy, and regulatory requirements
- Implement model explainability, bias detection, and monitoring mechanisms
5. Delivery Oversight
- Guide development teams through implementation
- Review code, models, and architectures to ensure quality
- Drive PoCs to production-ready solutions
Requirements
- Bachelor’s/Master’s degree in Computer Science, AI, Data Science, or related field
- 10+ years of experience in technology, with 5+ years in AI/ML architecture or solutioning
- Strong experience in:
- Machine Learning, Deep Learning, NLP, or Generative AI
- Designing distributed and scalable systems
- API-first and microservices architecture
- Hands-on experience with:
- Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
- Data platforms (Spark, Databricks, Snowflake)
Preferred Skills:
- Experience in Generative AI / LLM-based solutions
- Knowledge of RAG architectures, prompt engineering, vector databases
- Familiarity with DevOps / MLOps pipelines
- Experience in regulated industries (Banking, Healthcare, etc.)
- Strong communication and presentation skills
Key Competencies:
- Solution-oriented mindset
- Strategic thinking with hands-on execution ability
- Strong problem-solving and analytical skills
- Ability to influence senior stakeholders
Top Skills
AWS
Azure
Databricks
GCP
Kubeflow
Mlflow
Python
PyTorch
Sagemaker
Snowflake
Spark
TensorFlow
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