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Role Overview
The Agentic AI Engineer will be responsible for developing, optimizing, and deploying AI-powered autonomous agents on GCP. This role involves fine-tuning LLMs, designing AI workflows, integrating APIs, and building scalable AI applications using GCP’s AI/ML tools.
Key Responsibilities
The Agentic AI Engineer will be responsible for developing, optimizing, and deploying AI-powered autonomous agents on GCP. This role involves fine-tuning LLMs, designing AI workflows, integrating APIs, and building scalable AI applications using GCP’s AI/ML tools.
Key Responsibilities
- Develop agentic AI solutions using LangChain, AutoGen, CrewAI, and OpenAI APIs on GCP.
- Fine-tune LLMs (PaLM 2, Gemini, GPT) on Vertex AI for enterprise-specific applications.
- Implement multi-agent collaboration frameworks for intelligent automation and orchestration.
- Optimize vector search, embeddings, and retrieval mechanisms using Vertex AI Matching Engine and FAISS.
- Integrate AI agents with APIs, microservices, and event-driven architectures using Cloud Functions and Cloud Run.
- Design and implement data pipelines for AI workflows using BigQuery, Dataflow, and Cloud Composer.
- Work with Vertex AI Pipelines to manage ML model training, deployment, and continuous monitoring.
- Develop real-time AI applications leveraging Pub/Sub, Dataflow, and Cloud AI services.
- Implement observability, logging, and monitoring mechanisms for AI models in production.
- Collaborate with AI architects, ML engineers, and data scientists to optimize AI model performance.
- 7+ years in AI/ML development, software engineering, or data science.
- Strong experience with Python, LangChain, AutoGen, CrewAI, OpenAI API, and GCP AI tools.
- Expertise in LLMs, prompt engineering, RAG, embeddings, and hybrid search techniques.
- Hands-on experience with vector databases (Vertex AI Matching Engine, Pinecone, Weaviate, ChromaDB).
- Experience with Google Cloud Functions, Cloud Run, and Pub/Sub for event-driven AI applications.
- Familiarity with workflow orchestration tools like Cloud Composer (Airflow).
- Strong understanding of Google Cloud IAM, security best practices, and AI governance.
- Experience deploying AI models on GPUs/TPUs using GCP AI infrastructure.
- Experience with MLOps & LLMOps using Vertex AI Pipelines and Model Monitoring.
- Hands-on experience fine-tuning LLMs on domain-specific datasets.
- Knowledge of ethical AI, bias detection, and model interpretability.
- Contributions to open-source AI/ML projects or research papers.
Key Skills
Ranked by relevance
ai
cloud
gcp
microservices
composer
python
mlops
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- Posted
- Mar 27, 2025
- Type
- Full-time
- Level
- Associate
- Location
- Melbourne
- Company
- Virtusa
Industries
IT Services
IT Consulting
Categories
Engineering
Information Technology
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3 roles aligned with this opportunity
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View Job Details
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Backend Engineer - Remote
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