AI Engineer – Generative AI & GCP

AI Engineer - 2

Key Responsibilities

Model Development & Deployment: Design, train, fine-tune, and deploy scalable machine learning models and Generative AI solutions utilizing Vertex AI and the broader GCP ecosystem. Build and manage end to end deployment pipelines leveraging Vertex AI pipelines, Model Registry and Endpoints for real-time and batch inference.

GenAI, LLM Integration & Agent Development: Integrate commercial and open-source LLM APIs into enterprise applications. Design and develop agentic AI workflows using orchestration frameworks (e.g., LangChain, LlamaIndex), enabling multi-step reasoning, tool usage and autonomous decision making. Develop and optimize prompt engineering strategies to ensure high-quality, reliable, and secure model outputs, utilizing tools like Google ADK. Implement and manage Model Context Protocol (MCP) integrations.

Advanced AI Architectures: Leverage transformer architectures and build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embeddings for semantic search and contextual AI.

Data Pipelines: Architect and maintain robust, automated data pipelines for continuous model training, evaluation, and inference.

Cloud-Native Engineering: Design highly available, scalable, and secure cloud-native architectures to support enterprise-grade AI workloads.

MLOps Lifecycle: Implement MLOps best practices, including CI/CD for machine learning, model monitoring, and performance tuning in production environments.

Cloud Expertise: Proven hands-on experience with Google Cloud Platform (GCP), with deep operational knowledge of Vertex AI, BigQuery, Cloud Storage, and Cloud Functions/Run.

GenAI Proficiency: Strong theoretical and practical understanding of transformer architectures, text embeddings, and modern NLP techniques.

Programming: Advanced proficiency in Python and solid experience with software engineering principles (version control, testing, debugging).

Security and Governance: Demonstrated experience managing AI security, data privacy, and governance compliance within cloud environments. Development experience with AI Guardrails is required.

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