Senior AI Solution Architect

Job Summary:

Architect and govern enterprise-grade GenAI solutionsincluding RAG pipelines, intelligent agents, evaluations, and fine-tuning frameworks. This role ensures high-quality technical delivery, alignment with business needs, and the establishment of architecture standards for the AI Division.

Accountability & Responsibilities

Design end-to-end GenAI architectures (RAG, agents, LLM serving, orchestration).

Evaluate use cases and translate them into scalable architecture blueprints.

Define data, model, and integration architecture for GenAI workloads.

Ensure performance, reliability, and governance of all AI solutions.

Set architecture principles, coding standards, and MLOps patterns.

Conduct design reviews, risk assessments, and quality checks on all AI deliveries.

Oversee vendor/partner solutions to ensure compliance with internal standards.

Provide technical direction to delivery teams (data engineers, ML engineers, developers).

Own the technical success of GenAI projects and PoCs.

Manage architecture documentation, diagrams, and decision records (ADRs).

Align with business, product, and IT security teams to ensure business-ready solutions.

Communicate risks, trade-offs, and solution options clearly to non-technical leaders.

Evaluate emerging LLMs, tooling, and architectures.
Lead experimentation, benchmarks, and performance evaluations.
Drive continuous improvement of the AI platform and reusable GenAI components

Requirements

1 Required Experience

36 years in AI/ML engineering, software architecture, or distributed systems.

Proven leadership of complex AI/GenAI solution deliveries.

Experience in enterprise or regulated environments.

2 Technical Skills:

• RAG Design & Evaluation
  
• LangChain / LlamaIndex
  
• Vector Databases: Qdrant, Milvus, FAISS
  
• LLM Serving: vLLM, Triton, TorchServe
  
• Guardrails: evaluation, safety, observability
  
• Event-Driven & Microservices Architecture
  
• Python, Node.js, FastAPI
  
• Containerization: Docker, Kubernetes
  
• MLOps & Experiment Tracking: MLflow, Weights & Biases
  

3 Tools & Platforms:

• Python + Poetry
  
• FastAPI or Node.js
  
• Docker, Kubernetes
  
• MLflow, Weights & Biases
  
• GitHub Actions / CI/CD
  
• Cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI)
  

4 Certifications:

• NVIDIA NCA / ACE (Preferred)
  
• Cloud AI Architect Azure / AWS / GCP (Plus)