Solution Architect

Job Description :

Solution Architecture & Design
Architect scalable, secure, and cost-effective AI/ML and data solutions on Google Cloud Platform (Google Cloud Platform).
Design LLM-powered applications using Vertex AI, Gemini API, and RAG pipelines (e.g., with BigQuery, Vector Search, ChromaDB, or Elastic).
Define architecture blueprints integrating data pipelines, model deployment, and application services.
Build end-to-end GenAI workflows: data ingestion preprocessing embedding generation retrieval LLM orchestration evaluation.
AI/LLM Solution Implementation
Implement Retrieval-Augmented Generation (RAG) pipelines, including prompt engineering, model fine-tuning, and vector database integration.
Integrate LLMs with enterprise systems, APIs, and unstructured data (PDFs, logs, documents).
Optimize inference performance, model deployment, and data governance in production environments.
Data Engineering & Analytics
Work closely with data engineering teams to build pipelines using BigQuery, Dataflow, Dataproc, and Pub/Sub.
Ensure proper data lineage, transformation, and cataloging (e.g., with Data Catalog and Looker).
Design analytical models and dashboards supporting AI insights.
Cloud Governance & Best Practices
Implement Google Cloud Platform IAM, network, and security best practices.
Ensure solutions adhere to data privacy, compliance, and responsible AI frameworks.
Collaborate on cost optimization, MLOps, and CI/CD automation for AI workloads.
Collaboration & Stakeholder Management
Partner with business leaders, data scientists, and ML engineers to translate business goals into AI-powered solutions.
Provide architectural guidance, technical leadership, and mentoring for cloud and AI initiatives.
Required Skills and Experience
Education: Bachelor s or Master s in Computer Science, Data Engineering, AI/ML, or related field.
Cloud Expertise:
5 8 years in cloud architecture, with at least 3+ years on Google Cloud Platform (Google Cloud Platform).
Strong experience with Vertex AI, BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
AI/LLM Expertise:
Hands-on experience with LLMs (Gemini, GPT, Claude, or open-source models like LLaMA, Mistral).
Knowledge of RAG pipelines, prompt engineering, embedding models, and vector databases.
Familiarity with LangChain, Vertex AI Search, ChromaDB, FAISS, or Pinecone.
Understanding of fine-tuning, model evaluation, and context window optimization.
Programming:
Proficiency in Python (must-have) and familiarity with SQL, Java, or Go.
Experience building microservices or APIs to integrate AI models.
MLOps / DevOps:
Experience with Vertex AI Pipelines, Cloud Build, Terraform, or Kubernetes (GKE).
Knowledge of CI/CD pipelines for model deployment.
Soft Skills:
Excellent communication, presentation, and stakeholder management skills.
Strong analytical and problem-solving mindset with the ability to design solutions end-to-end.
Preferred / Nice-to-Have
Google Cloud Platform Professional Certifications:
Professional Cloud Architect
Professional Machine Learning Engineer
Experience implementing Knowledge Graph + RAG systems.
Familiarity with data security frameworks and Responsible AI principles.
Experience deploying multi-modal LLMs (text, image, speech).

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.