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:
- 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.
Required Skills & Experience:
- 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.
Preferred Qualifications:
- 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 toopen-source AI/ML projects or research papers.
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:
- 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.
Required Skills & Experience:
- 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.
Preferred Qualifications:
- 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 toopen-source AI/ML projects or research papers.
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 27,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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