AI Architect

Job Description – Senior AI Consultant

We are seeking a highly skilled Senior AI Consultant to oversee, evaluate, and guide our development teams in the successful execution of AI-driven projects. This role requires end-to-end ownership of solution quality, technical evaluation, and delivery alignment. The consultant must ensure that all AI solutions are architecturally sound, feasible, scalable, and aligned with business goals.

Key Responsibilities

1. Technical Evaluation & Solution Architecture

- Evaluate proposed AI/ML use cases for feasibility, scalability, cost, and performance.
- Design end-to-end AI architecture: data ingestion, pipelines, model training, inference, deployment, and monitoring.
- Review models, prompts, LLM workflows, RAG pipelines, and data engineering processes.
- Identify risks, gaps, and remediation steps in ongoing AI implementations.

2. Delivery Oversight & Guidance

- Provide continuous technical supervision to development teams.
- Conduct weekly/bi-weekly reviews (physical or virtual) of models, code, datasets, and workflows.
- Ensure compliance with AI/ML best practices, MLOps standards, and data security.
- Troubleshoot complex AI issues and guide teams toward resolution.
- Validate and sign-off on major releases, deployments, and production rollouts.

3. AI Strategy, Planning & Vendor Evaluation

- Assist in shaping organizational AI strategy and roadmap.
- Evaluate third-party AI vendors, tools, and technologies; provide recommendations.
- Suggest scalable Private/Public cloud frameworks (AWS/Azure/GCP) for implementation.
- Conduct POCs and benchmarking exercises when required.

4. Documentation & Reporting

- Prepare architecture diagrams, audit reports, model evaluation summaries, and risk assessments.
- Provide concise weekly reports on progress, blockers, and upcoming priorities.
- Present findings to leadership and project management teams.

5. Mentorship & Capability Building

- Train and mentor internal developers on LLMs, generative AI, prompt engineering, ML pipelines, etc.
- Conduct workshops or tech sessions during on-site visits.
- Build internal AI/ML guidelines for long-term adoption.