AI/ML Engineer/Architect
Why This Role Stands Out
This hybrid role offers a significant opportunity to architect and deploy cutting-edge AI/ML solutions, including Generative AI, shaping the future of technology. You'll thrive here if you possess extensive experience in machine learning, deep learning, and cloud platforms, leveraging your Python expertise to build production-grade applications. Apply now to join a forward-thinking team and make a real impact.
Quick Overview
Job Description
Role: AI/ML Engineer/Architect
Location: Charlotte, NC
Job Type: W2 and C2C
Experience Required: 12+ Years
Job Summary
We are seeking an experienced AI/ML Engineer to design, develop, and deploy scalable Artificial Intelligence and Machine Learning solutions. The ideal candidate will have strong expertise in machine learning, deep learning, Generative AI, and cloud platforms, with experience building production-grade AI applications.
Required Skills
- 8+ years of experience in AI/ML Engineering
- Strong programming skills in Python
- Hands-on experience with Machine Learning, Deep Learning, and Generative AI (LLMs)
- Experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Strong understanding of NLP, RAG, prompt engineering, and vector databases
- Experience with REST APIs and model deployment
- Knowledge of Docker, Kubernetes, and CI/CD pipelines
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
- Familiarity with SQL/NoSQL databases
- Excellent analytical, communication, and problem-solving skills
Preferred Qualifications
- Experience with OpenAI, Azure OpenAI, LangChain, LlamaIndex, or Hugging Face
- Experience deploying ML models using MLOps tools such as MLflow or Kubeflow
- Knowledge of data engineering tools such as Spark or Databricks
Responsibilities
- Design, build, and deploy AI/ML models for enterprise applications
- Develop and optimize Generative AI and LLM-based solutions
- Build scalable APIs and AI services for production environments
- Collaborate with data engineers, software developers, and business stakeholders
- Optimize model performance, scalability, and reliability
- Follow best practices for MLOps, monitoring, testing, and model governance
Thanks & Regards,
Aditya Kumar
Email:
Web:
Skills
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