Gen AI Developer (Experience 8 - 15 Years)

Gen AI Developer (Experience 8 - 15 Years)

(Bellevue, Seattle, Everett, Renton, WA )/ (Richardson, Plano, Dallas, TX)

Fulltime - Need only Independent VISA Holders

Job Description

Must Have Technical/Functional Skills

Experience:

Experience in machine learning, data science, or related fields.

Hands-on experience with generative models (e.g., GANs, VAEs, diffusion models) and LLMs.

Experience with ML frameworks (e.g., TensorFlow, PyTorch, JAX).

Familiarity with cloud environments (AWS Sagemaker, Azure ML, or GCP AI Platform).

Strong programming skills in Python (preferred) or similar languages.

Proficiency in using libraries like Hugging Face, LangChain, or NVIDIA BioNeMo.

Knowledge of Docker, Kubernetes, and CI/CD pipelines for deployment.

Understanding of NLP, computer vision, or multimodal AI techniques.

Strong problem-solving skills and a passion for AI-driven innovation.

Experience with Retrieval-Augmented Generation (RAG) techniques and vector databases (e.g., Pinecone, Weaviate, Milvus).

Responsibilities

Model Development and Deployment:

Design, fine-tune, and deploy generative AI models (e.g., Llama, GPT, Stable Diffusion) for various applications.

Train and optimize large language models (LLMs) for tasks such as natural language understanding, summarization, and conversational AI.

2. Data Pipeline Management:

Develop and maintain robust data pipelines for model training and inference.

Clean, preprocess, and manage large-scale datasets to support AI projects.

3. Integration and Scalability:

Implement ML models in production environments using tools like TensorFlow, PyTorch, or Hugging Face.

Optimize models for performance, scalability, and cost-efficiency on cloud platforms (AWS, Azure, GCP).

4. Collaboration and Innovation:

Work with product managers, data engineers, and software developers to align AI solutions with business objectives.

Stay updated with the latest research and advancements in Gen AI and ML.

5. System Monitoring and Maintenance:

Monitor deployed models for performance and accuracy; implement retraining and versioning strategies.

Ensure systems meet ethical, privacy, and compliance standards