AI Engineer

Job Title: AI Engineer

• Role Overview
  We are looking for an AI Integration Engineer to build everyday AI solutions for enterprise teams. Operating within a tight-knit delivery pod, you will transform functional specifications into production-ready AI applications. You will connect enterprise AI platforms to real-time customer systems, structure critical knowledge bases, and author reusable agent workflows.
  This role focuses heavily on enterprise AI platform orchestration and data integration rather than training raw models from scratch. You will work alongside a Senior Engineer (for technical mentorship) and an Architect (who owns the client relationship), giving you strong air cover to scale your skills rapidly.

• Key Responsibilities (What You Will Do)

• End-to-End AI Deployment: Build, configure, and ship AI use cases from sandbox to production based on clear product specifications.

• Enterprise Connectivity: Configure and test platform connectors for tools like Microsoft 365, Salesforce, Snowflake, and SharePoint, managing authentication (OAuth2, API keys) and precise access scoping.

• Data Grounding & Knowledge Structuring: Architect and load the data sources anchoring our AI agents to ensure factual accuracy and secure data grounding.

• Workflow & Agent Behavior Authoring: Create reusable skills, distinct behavioral/tone profiles, and multi-step agent workflows on the client's enterprise AI platform.

• Prompt Engineering & Iteration: Refine prompts and configurations continuously against strict written acceptance criteria until outputs clear peer review.

• Technical Documentation: Write transparent build documentation and log architectural decisions to ensure all work is highly repeatable and auditable.

• Proactive Collaboration: Identify gaps in technical specifications early and surface questions instead of executing on assumptions.

• Candidate Requirements (What You Need)

• Professional Experience: 2 to 4 years of professional software engineering experience.

• Core Programming: Strong proficiency in Python, including the ability to read, debug, and extend complex codebase systems independently.

• API Integration: Practical experience working with REST and JSON APIs, including secure auth patterns (OAuth2, service accounts, and API keys).

• AI Foundations: Hands-on experience working with LLM APIs or enterprise AI platforms, paired with a disciplined approach to prompt iteration against defined quality metrics.

• Modern Tooling: Familiarity with at least one major cloud provider (AWS, Azure, or GCP) and Git-based development workflows.

• Communication Skills: Exceptional written English skills, optimized for asynchronous communication with internal teams and external client counterparts.

• Mindset: Comfort with non-deterministic outputs; you possess the patience to systematically test, measure, and iterate on AI behavioral systems.

• Preferred Qualifications (Nice to Have)

• Experience utilizing enterprise AI platforms (e.g., Writer, Glean, Microsoft Copilot Studio).

• Exposure to tool/connector frameworks such as MCP (Model Context Protocol).

• Fundamental understanding of RAG (Retrieval-Augmented Generation), vector search, or knowledge graphs.

• Familiarity with machine learning frameworks (PyTorch, TensorFlow, or JAX).

• Conceptual understanding of deep learning, transformer architectures, or model fine-tuning.

• Awareness of MLOps tooling and the broader machine learning lifecycle.

• Insight into Responsible AI principles (evaluation frameworks, explainability, bias mitigation).

• Prior experience in consulting, client-facing engineering, or enterprise SaaS domains (ServiceNow, Salesforce, Snowflake).