Senior AI Solution Architect

® Job Summary:

Architect and govern enterprise-grade GenAI solutions—including 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

· 3–6 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)