GEN AI​/ML Architect - GCP Cloud

Senior Technology Architect | Cloud Platform | Google Machine Learning -- GEN AI Engineer

Work Location & Reporting Address:
Dallas, TX or Charlotte, NC (Onsite-Hybrid. Will consider candidates willing to relocate to client’s location)

Contract duration: 6 months Target

Start Date:

01 Feb 2026 Does this position require Visa independent candidates only? Yes

Must Have

Skills:

GEN AI Agentic AI ML Ops Python ML Data Science RAG LLM

Nice to Have

Skills:

GCP Prompt Engineering

Key Responsibilities:

• Design and implement Generative AI models for text, image, or multimodal applications.
• Develop prompt engineering strategies and embedding-based retrieval systems.
• Integrate Gen AI capabilities into web applications and enterprise workflows.
• Build agentic AI applications with context engineering and MCP tools.

Required Skills &

Qualifications:

• 15+ years of hands-on experience in AI, Data science, ML, GEN AI.
• Strong hands on experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines
• Strong MLOps/LLMOps experience with CI/CD automation
• Extensive experience with Lang Chain, Lang Graph, and agentic AI patterns including routing, memory, multi-agent orchestration, guardrails, and failure recovery.
• Experience in Cloud-native engineering across AWS (Sage Maker, Lambda, ECS/Fargate, S3, API Gateway, Step Functions) and GCP (Vertex AI) for scalable AI delivery
• Experience in Developing microservices and API development using FastAPI, REST APIs, Pydantic/JSON schemas, Docker, and Kubernetes for low-latency serving.
• Strong Hands-on experience with vector databases and semantic search technologies including Pinecone, FAISS, ChromaDB, and embedding lifecycle management
• Strong proficiency in Python and AI/ML frameworks (PyTorch, Tensor Flow).
• Hands on experience using session and memory for building multi-agent systems along with using MCP tools.
• Hands-on experience with LLMs, transformers, and Hugging Face ecosystem.
• Knowledge and experience with vector databases and RAG technique for semantic search.
• Familiarity with cloud AI services (AWS Sage Maker, Azure OpenAI, GCP Vertex AI).
• Understanding of MLOps practices for scalable AI deployment.