AI Engineer

• Design, build, and optimize the agentic stack for conversational and autonomous agents
• Architect and maintain end-to-end LLM features, including model orchestration, routing, monitoring, logging, and evaluation
• Develop and improve ML infrastructure, including observability, load balancing, scaling, and deployment workflows
• Build datasets and evaluation pipelines for ASR, LLM, and agentic systems
• Collaborate with Software Engineers to establish scalable, observable, and automated infrastructure around ML services
• Collaborate with Product Managers to assess feasibility, refine specifications, and reliably deliver ML features
• Contribute to the technical strategy, model and tool selection, architecture decisions, and roadmap for the agentic platform
• Stay current with research, frameworks, and open-source developments in LLMs, agentic systems, and evaluation methodologies
• Participate in the recruitment process, including screening calls, onsite case studies, team-fit interviews, final interviews, and, when applicable, reference checks

Requirements

• 2–5 years of experience in ML Engineering or Data Science
• Strong proficiency in Python
• Experience with lower-level languages such as Java or C is a plus; a purely scripting-focused profile will not be sufficient
• Solid foundation in ML, including model types, data science best practices, and evaluation methods
• Strong understanding of LLMs, including prompt design, evaluation, and cost/performance trade-offs
• Experience with agentic systems, including tool execution, workflows, memory architectures, and LangChain / LangGraph or equivalent
• Exposure to MCP (Model Context Protocol), DeepEval, FastAPI, and Uvicorn
• Understanding of NLP concepts, including transcription, embeddings, and language understanding
• Intellectual curiosity
• Strong sense of ownership and communication skills

Core Competencies

Demonstrates expertise in Machine Learning Engineering and Data Science, with a strong proficiency in Python and a solid foundation in LLMs and agentic systems. Capable of architecting and optimizing ML infrastructure while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

• Machine Learning Engineering
• Python Proficiency
• LLM Understanding
• Agentic Systems Experience
• Model Evaluation Methods

Hard Skills

• Machine Learning
• Data Science
• Model Orchestration
• Prompt Design
• Evaluation Methodologies
• Load Balancing
• Scaling
• Deployment Workflows
• NLP Concepts
• Tool Execution

Soft Skills

• Intellectual Curiosity
• Strong Sense of Ownership
• Communication Skills

Industry Keywords

• Conversational Agents
• Autonomous Agents
• Observability
• Data Science Best Practices
• Evaluation Pipelines

Tools & Technologies

• LangChain
• LangGraph
• MCP
• DeepEval
• FastAPI
• Uvicorn