Machine Learning Engineer

Job Title:

Job Type:

Location

Job Summary

Join our customer's dynamic team as an LLM Engineer, where you’ll leverage advanced language models and cutting-edge frameworks to build high-impact AI solutions. As a core contributor, you will help architect, deploy, and optimize LLM-powered applications using modern tools and methodologies in a collaborative, agile environment.

Key Responsibilities

• Design, develop, and deploy applications powered by large language models (LLMs) to solve real-world problems.
• Integrate and optimize LLM APIs with frameworks such as LangGraph and DSPy for enhanced tool orchestration.
• Implement and maintain vector databases (such as Qdrant, Milvus, or Pgvector) to support scalable data retrieval and storage operations.
• Collaborate effectively with team members using Azure DevOps, ensuring streamlined CI/CD pipelines and agile best practices.
• Work closely with cross-functional teams to understand requirements, deliver solutions, and contribute to architectural decisions.
• Troubleshoot, monitor, and enhance MCP server environments to ensure robust and reliable application performance.
• Continuously document solutions and communicate technical concepts clearly in both written and verbal formats.

Required Skills and Qualifications

• Proven experience developing with large language models (LLMs) in production environments.
• Hands-on proficiency with LangGraph and DSPy for advanced language model application workflows.
• Strong knowledge of LLM API integration and practical tool use in AI/ML pipelines.
• Expertise in deploying and managing vector databases such as Qdrant, Milvus, or Pgvector.
• Familiarity with Azure DevOps and agile development processes for efficient project execution.
• Demonstrated skill in maintaining and optimizing MCP server environments.
• Exceptional written and verbal communication skills, with a passion for clear, effective information exchange.

Preferred Qualifications

• Prior experience working in remote, distributed teams on AI-driven or SaaS products.
• Background in developing and deploying AI tools within enterprise-grade cloud ecosystems.
• Contributions to open-source LLM or AI infrastructure projects.