AI Architect

The AI Architect is responsible for defining, designing, and governing end‑to‑end AI and GenAI solutions that are scalable, secure, and aligned with business strategy. This role bridges business needs, data engineering, ML engineering, and cloud platforms to deliver production‑grade AI systems across the enterprise.

Technical Skills

• Strong experience in AI architecture, machine learning, and deep learning concepts.
• Hands‑on knowledge of Python and ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
• Experience with GenAI and LLM ecosystems (OpenAI, Azure OpenAI, Anthropic, open‑source models).
• Expertise in MLOps tools and practices (CI/CD, model registries, monitoring).
• Experience with data platforms (Data Lakes, Snowflake, BigQuery, Databricks).
• Knowledge of API‑based AI integration, MCP, microservices, and event‑driven architectures.

Cloud & DevOps

• Strong experience with at least one major cloud platform: Azure, AWS, or GCP.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Understanding of infrastructure‑as‑code and DevSecOps practices.

Experience

• 8–12+ years of overall IT experience, with 3–5+ years in AI/ML solution or platform architecture roles.
• Proven experience delivering AI solutions into production at scale.

AI Strategy & Architecture

• Define enterprise AI/ML and GenAI architecture roadmaps aligned with business objectives.
• Design scalable, reusable, and secure AI solution architectures (traditional ML, deep learning, LLM‑based systems).
• Evaluate and select AI platforms, frameworks, and cloud services.

Solution Design & Delivery

• Architect end‑to‑end AI solutions encompassing data ingestion, feature engineering, model training, inference, monitoring, and feedback loops.
• Lead architecture for use cases such as predictive analytics, NLP, computer vision, recommendation systems, and AI agents.
• Design architectures leveraging LLMs, vector databases, RAG frameworks, fine‑tuning, and prompt engineering.

Governance, Security & Compliance

• Define AI governance standards including model lifecycle management, data privacy, bias mitigation, explainability, and responsible AI.
• Ensure compliance with enterprise security, regulatory, and ethical AI standards.
• Establish best practices for model versioning, monitoring, and drift management.

Stakeholder & Technical Leadership

• Act as a trusted advisor to business leaders, product owners, and delivery teams on AI feasibility and value realization.
• Translate business problems into AI solution designs and technical blueprints.
• Mentor data scientists, ML engineers, and architects; review solution designs and implementations.

• Experience designing AI solutions in regulated industries (Insurance or BFSI)
• Knowledge of Responsible AI, explainable AI (XAI).
• Experience with AI agent frameworks and autonomous workflows.
• Strong architectural thinking and problem‑solving skills
• Ability to balance innovation with enterprise governance
• Excellent stakeholder communication and technical storytelling
• Leadership and mentoring mindset