Lead Kubernetes Engineer
Position: Python lead with gen AI Location: Charlotte, NC. (hybrid) Job Type: Contract At this time we are not considiring H1B.
About the Role
We are seeking an experienced Python Lead with strong expertise in Generative AI to spearhead the design, development, and deployment of AI-powered solutions. This leadership role involves guiding a team of developers, collaborating with data scientists, and driving innovation by integrating cutting-edge AI models into enterprise applications.
Key Responsibilities
- Lead a team of Python developers in building scalable AI-driven applications.
- Architect and implement solutions using Generative AI models (LLMs, diffusion models, transformers).
- Collaborate with data scientists and ML engineers to fine-tune and deploy models in production.
- Design APIs and microservices to enable AI-powered features such as text generation, summarization, and multimodal applications.
- Ensure best practices in coding, testing, and deployment across the team.
- Mentor junior developers and foster a culture of innovation and continuous learning.
- Optimize AI applications for performance, scalability, and ethical use.
- Stay ahead of industry trends in Generative AI and recommend adoption strategies.
Required Skills & Qualifications
- Bachelor's/Master's degree in Computer Science, Engineering, or related field.
- 7+ years of experience in Python development, with at least 2 years in a leadership role.
- Strong understanding of Generative AI concepts (LLMs, embeddings, transformers).
- Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face).
- Proven track record of deploying AI models into production environments.
- Familiarity with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization (Docker, Kubernetes).
- Knowledge of databases (MongoDB, PostgreSQL, MySQL).
- Excellent leadership, communication, and problem-solving skills.
Preferred Skills
- Experience with prompt engineering and fine-tuning large language models.
- Knowledge of MLOps practices (CI/CD for ML, model monitoring).
- Exposure to NLP, computer vision, or multimodal AI applications.
- Understanding of Agile/Scrum methodologies.
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