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AI/ML Architect - W2 only - Grand Rapids MI at FIRSTNET GLOBAL LLC

Berwyn, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Aug, 26

Salary

70.0

Posted On

03 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, LLMs, RAG, NLP, Deep learning, Python, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, GCP, Data engineering, MLOps, Vector databases, Microservices

Industry

Information Services

Description

Benefits: Company parties Competitive salary Opportunity for advancement Hi Professionals Hope you are doing good Job Title: AI/ML Architect (Generative AI & LLMs) Location: Grand Rapids, MI (Locals Only) Employment Type: W2 Only Experience: 12+ Years Job Summary Seeking an experienced AI/ML Architect to design and deliver scalable AI solutions, focusing on Generative AI, LLMs, and enterprise ML systems. Key Responsibilities Define AI/ML architecture strategy Design end-to-end solutions (GenAI, LLMs, ML models) Build scalable data pipelines (batch & streaming) Implement MLOps (deployment, monitoring, versioning) Collaborate with data/AI teams and drive best practices Evaluate and adopt new AI technologies Required Skills 10+ years in software/data science; 3–5+ years in AI/ML architecture Strong experience in RAG, LLMs, NLP, deep learning Python with TensorFlow / PyTorch / Scikit-learn Cloud: AWS / Azure / GCP Data engineering (ETL, data lakes, streaming) MLOps: MLflow, Kubeflow, SageMaker, Vertex AI APIs, microservices, distributed systems Vector databases (Pinecone, FAISS, etc.) DevOps / CI-CD for ML AI governance & compliance Thanks and Regards, Abhishek FIRSTNET GLOBAL LLC +19729687034 Email: rec3@firstnetglobal.com LinkedIn: linkedin.com/in/abhishek-p-4161a521b 6010 W Spring Creek Pkwy, Ste 211, Plano, TX 75024

Responsibilities

The AI/ML Architect will define the architecture strategy and design end-to-end solutions for Generative AI, LLMs, and enterprise ML systems. They are also responsible for building scalable data pipelines and implementing MLOps practices.