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Specialized GenAI and Vertex AI requirements reduce pool, but mid-level ML role remains moderately competitive.
Advanced GenAI, Vertex AI and MLOps requirements make the role highly domain-specific and less transferable.
Mandatory Vertex AI, GCP, MLOps, GenAI and security experience increases filtering strictness.
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Design, train, fine-tune, and deploy scalable Generative AI models leveraging Vertex AI and GCP, including building deployment pipelines for real-time and batch inference.
Integrate and develop agentic AI workflows and LLM APIs for multi-step reasoning and autonomous decision making using orchestration frameworks like LangChain.
Implement cloud-native scalable architectures and MLOps best practices (CI/CD, monitoring, performance tuning) for enterprise-grade AI workloads on GCP.
Hands-on experience with Google Cloud Platform technologies including Vertex AI, BigQuery, Cloud Storage, and Cloud Functions/Run.
Proficient in Python programming with solid software engineering skills (version control, testing, debugging).
Experience with Generative AI concepts, transformer architectures, embeddings, and modern NLP techniques.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing secure AI and data governance in cloud environments, including use of AI Guardrails.
Skilled in building robust automated data pipelines for continuous model training, evaluation, and inference.
Strong background in integrating and orchestrating LLM APIs and developing advanced AI solutions with vector databases and retrieval-augmented generation pipelines.