IT
AgentOps
ItmaginationAbout Us
At Virtusa, every innovator has the potential to transform and lead in a digital world—but unlocking that potential takes more than technology; it takes a trusted partner who combines engineering excellence, creativity, and an AI-first mindset.
Together, we co-create solutions that help businesses grow faster, operate smarter, and make experiences better with technology.
Job Description
This is a remote position.
We are seeking an experienced AgentOps(Machine Learning Engineer) to adopt AgentOps framework into production-grade GenAI and Agentic AI solutions. The ideal candidate has strong Python engineering skills(API provision, production-grade code refactoring), hands-on experience with AgentOps frameworks(LangSmith/LangFuse/Phoenix etc).
Requirements
• 5+ years of experience in Software Engineering, Machine Learning Engineering, or AI Engineering.
• Strong proficiency in Python and modern software engineering best practices.
• Hands-on experience building and deploying production-grade GenAI, Agentic AI, or LLM-powered applications.
• Experience with AgentOps platforms such as LangSmith, Langfuse, Databricks, MLflow, or similar observability and evaluation tools.
• Strong understanding of RAG, prompt engineering, tool calling, agent memory, and multi-agent architectures.
• Experience with agent orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar technologies.
• Strong debugging, troubleshooting, and performance optimization skills.
• Excellent communication and collaboration skills, with the ability to work effectively across cross-functional teams.
Preferred Qualifications
• Experience with MLOps, LLMOps, or AgentOpspractices in enterprise environments.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and container technologies (Docker, Kubernetes).
• Experience implementing AI evaluation, monitoring, and governance frameworks.
• Strong proficiency in Python and modern software engineering best practices.
• Hands-on experience building and deploying production-grade GenAI, Agentic AI, or LLM-powered applications.
• Experience with AgentOps platforms such as LangSmith, Langfuse, Databricks, MLflow, or similar observability and evaluation tools.
• Strong understanding of RAG, prompt engineering, tool calling, agent memory, and multi-agent architectures.
• Experience with agent orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar technologies.
• Strong debugging, troubleshooting, and performance optimization skills.
• Excellent communication and collaboration skills, with the ability to work effectively across cross-functional teams.
Preferred Qualifications
• Experience with MLOps, LLMOps, or AgentOpspractices in enterprise environments.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and container technologies (Docker, Kubernetes).
• Experience implementing AI evaluation, monitoring, and governance frameworks.