Software AI Engineer Mid-Level, Context Engineering

As an AI Platform Engineer (SDE 2), you will be a hands-on developer responsible for building and maintaining the core software components that power our AI and context infrastructure. You will work on the "Context Layer"-the plumbing that connects enterprise data to LLMs-ensuring that our AI agents have the right information at the right time. This role is ideal for a strong software engineer who wants to specialize in the operational side of AI, focusing on high-quality code, automated delivery, and cloud-native systems.

• Feature Development: Implement and maintain core services for the AI Data Lakehouse, focusing on efficient data retrieval and storage optimizations for AI workflows.

• Pipeline Automation: Build and support CI/CD pipelines to automate the deployment of AI models, prompt templates, and infrastructure updates.

• Agentic Support: Develop and test tool-execution environments and API interfaces that allow AI agents to interact with internal business systems safely.

• Operational Excellence: Participate in on-call rotations and troubleshooting to ensure platform reliability. Write unit tests, integration tests, and documentation for new features.

• Context Retrieval: Work on the "Context Fabric" to implement search and retrieval patterns (like RAG) that help agents access secure enterprise data.

• Cloud Management: Assist in managing cloud resources across AWS and Azure, ensuring environments are cost-effective and secure.

Software Engineering Foundation

• Experience: 3 years of professional software development experience.

• Core Skills: Strong proficiency in Python and either Java or Scala. You write clean, maintainable, and well-documented code.

• API Development: Experience building and consuming RESTful APIs or gRPC services.

• Database Basics: Understanding of relational databases (Postgres/MySQL) and familiarity with how data is stored in a distributed environment.

Cloud & CI/CD Mastery

• Cloud Consoles: Hands-on experience navigating the AWS or Azure Management Consoles. You should be comfortable managing basic services like IAM, S3/Blob, and compute instances.

• Infrastructure-as-Code (IaC): Basic experience with Terraform. You can read, modify, and deploy infrastructure modules.

• CI/CD Tools: Familiarity with GitHub Actions, GitLab CI, or Jenkins. You understand how to automate the build-test-deploy lifecycle.

• Observability: Basic experience with monitoring tools like Prometheus, Grafana, or cloud-native solutions (CloudWatch/Azure Monitor).

AI & Agentic Interests (Specialized Focus)

• LLM Awareness: Familiarity with LLM concepts and frameworks like LangChain or LlamaIndex. You've experimented with or built basic RAG-based applications.

• Emerging Protocols: A desire to learn and implement new standards like the Model Context Protocol (MCP).

• Agentic Workflows: Interest in how autonomous agents function, including tool-use (function calling) and state management.

• Data Retrieval: Basic understanding of vector databases (e.g., Pinecone, Milvus) and how search impacts AI performance.

• Team Player: Ability to work effectively in an agile environment, participating in sprint planning and daily stand-ups.

• Continuous Learner: A strong desire to stay current with the rapidly changing AI and cloud landscape.

• Education: Bachelor's degree in Computer Science, Software Engineering, or a related technical field.