AI Engineer

Flexday AI

Posted 2mo ago

The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.

Excellent written and verbal communication skills in English
Demonstrated ability to explain technical concepts to non-technical business stakeholders
Comfort facilitating working sessions and leading solution walkthroughs
Strong sense of ownership, accountability, and follow-through
Ability to operate independently in ambiguous, fast-moving environments
Solid working knowledge of AI agents, agentic workflows, and patterns
Hands-on experience building agents using frameworks such as LangGraph, LangChain, OpenAI Agents SDK, Semantic Kernel, or equivalent
Understanding of how to evaluate, debug, and productionize agent behavior
Prompt engineering, RAG, fine-tuning, and LLM integration
Classical ML, feature engineering, model training, evaluation, and deployment
CNNs, detection, segmentation, vision transformers, or OCR
2 to 5+ years of hands-on software development experience, primarily in Python
Proven contribution to large-scale programs deployed in enterprise environments
Understanding of full-stack development, REST APIs, and microservices
Disciplined approach to code quality, testing, and documentation
Hands-on experience developing and deploying on AWS or Azure
Experience working with large datasets and data pipelines
Familiarity with Docker and Git
Bachelor’s or Master’s degree in computer science, Engineering, Data Science, or a related technical field

Familiarity with Model Context Protocol (MCP) servers and multi-agent orchestration patterns
GCP is a plus
A PhD or equivalent qualification is highly valued
Enterprise Platforms: Amazon Bedrock, Amazon Bedrock AgentCore, Microsoft Copilot, Copilot Studio, Azure OpenAI, Azure AI Foundry, or Google Vertex AI
Agent Frameworks and Protocols: LangGraph, LangChain, OpenAI Agents SDK, or similar
Model Context Protocol (MCP) servers and multi-agent orchestration
RAG and Retrieval Systems
Vector databases (Pinecone, Weaviate, Qdrant, pgvector)
Embedding pipelines, semantic search, and document intelligence
Data Engineering: Databricks, PySpark, Microsoft Fabric, Synapse, or similar platforms
DevOps and MLOps: Understanding of CI/CD, model deployment, monitoring, and observability
Hands-on experience with any industry-leading MLOps tools and platforms

Skills