Themesoft Inc.
Talent Acquisition Manager at Themesoft Inc.
Client expectation – GCP Sol. Architect / Migration experience / Consulting to client.
Location : Ashburn, VA (3 days a week)
Key Responsibilities
Architect scalable, secure, and cost-effective AI/ML and data solutions on Google Cloud Platform (GCP).
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.
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.
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.
Implement GCP 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.
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.
5–8 years in cloud architecture, with at least 3+ years on Google Cloud Platform (GCP).
Strong experience with Vertex AI, BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
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.
Proficiency in Python (must‑have) and familiarity with SQL, Java, or Go.
Experience building microservices or APIs to integrate AI models.
Experience with Vertex AI Pipelines, Cloud Build, Terraform, or Kubernetes (GKE).
Knowledge of CI/CD pipelines for model deployment.
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
GCP Professional Certifications: Professional Cloud Architect, Professional Machine Learning Engineer.
Familiarity with data security frameworks and Responsible AI principles.
Experience deploying multi‑modal LLMs (text, image, speech).
Seniority level: Mid‑Senior level
Employment type: Contract
Job function: Information Technology
Industries: IT Services and IT Consulting
#J-18808-Ljbffr
Location : Ashburn, VA (3 days a week)
Key Responsibilities
Architect scalable, secure, and cost-effective AI/ML and data solutions on Google Cloud Platform (GCP).
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.
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.
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.
Implement GCP 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.
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.
5–8 years in cloud architecture, with at least 3+ years on Google Cloud Platform (GCP).
Strong experience with Vertex AI, BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
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.
Proficiency in Python (must‑have) and familiarity with SQL, Java, or Go.
Experience building microservices or APIs to integrate AI models.
Experience with Vertex AI Pipelines, Cloud Build, Terraform, or Kubernetes (GKE).
Knowledge of CI/CD pipelines for model deployment.
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
GCP Professional Certifications: Professional Cloud Architect, Professional Machine Learning Engineer.
Familiarity with data security frameworks and Responsible AI principles.
Experience deploying multi‑modal LLMs (text, image, speech).
Seniority level: Mid‑Senior level
Employment type: Contract
Job function: Information Technology
Industries: IT Services and IT Consulting
#J-18808-Ljbffr