What are the responsibilities and job description for the QA Automation Lead (AI/ML & GenAI) position at Jobs via Dice?
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# W2 Requirement
Quality Engineering Experience: 8 10 years overall (minimum 4 years in AI/ML and GenAI) Required Skills Strong proficiency in Python (Fast API, Flask, async programming, RESTful APIs).
Proven experience in AI/ML pipelines data preprocessing, model training, fine-tuning, and deployment (TensorFlow, PyTorch, Scikit-learn).
Must have hands-on with GenAI frameworks (Lang Chain, LlamaIndex, Lang Graph, Neo4j, Bedrock, etc.).
Hands-on experience implementing the RAG pipelines - Knowledge Management with vector databases, embeddings, Graphs.
Working knowledge of prompt engineering, optimisation, and fine-tuning. Familiarity with Graph databases (Neo4j) or agent-based architectures.
Architect and code in Python for scalable, reusable, and high-performing systems. Strong grounding in software quality engineering concepts and automation frameworks.
Solid understanding of CI/CD, containerization (Docker/Kubernetes), and cloud(AWS) deployment.
# W2 Requirement
Quality Engineering Experience: 8 10 years overall (minimum 4 years in AI/ML and GenAI) Required Skills Strong proficiency in Python (Fast API, Flask, async programming, RESTful APIs).
Proven experience in AI/ML pipelines data preprocessing, model training, fine-tuning, and deployment (TensorFlow, PyTorch, Scikit-learn).
Must have hands-on with GenAI frameworks (Lang Chain, LlamaIndex, Lang Graph, Neo4j, Bedrock, etc.).
Hands-on experience implementing the RAG pipelines - Knowledge Management with vector databases, embeddings, Graphs.
Working knowledge of prompt engineering, optimisation, and fine-tuning. Familiarity with Graph databases (Neo4j) or agent-based architectures.
Architect and code in Python for scalable, reusable, and high-performing systems. Strong grounding in software quality engineering concepts and automation frameworks.
Solid understanding of CI/CD, containerization (Docker/Kubernetes), and cloud(AWS) deployment.