AI & Analytics Engineer

ProArch is looking for a motivated and curious AI & Analytics Engineer (Intern or Early Career) to join our fast-moving team building CSP Management Platform— a next-generation, agent-based AI platform designed to deliver insights, automate tasks, and support decision-making. You'll work at the intersection of data engineering, GenAI and Analytics, helping us build the intelligence layer for business workflows.

Your responsibilities will include integrating AI solutions into existing data pipelines, conducting exploratory data analysis, and utilizing machine learning techniques to extract actionable insights from complex datasets. You will play a crucial role in the entire data lifecycle, from data collection to model deployment.

Key Responsibilities:

• Assist in building and maintaining data pipelines using Python, SQL, and APIs (e.g., HubSpot, Autotask)
• Work on AI agent prototypes using tools like OpenAI APIs and LangChain
• Create internal dashboards and analytics tools using Power BI, Streamlit, or similar frameworks
• Support LLM prompt design and testing across different business workflows
• Contribute to model evaluation and observability efforts, including metric tracking
• Collaborate with cross-functional teams to integrate data, models, and analytics into our product
• Stay up-to-date with the latest AI trends and technologies to continuously improve solutions.

Requirements

• Strong foundation in Python and SQL
• Hands-on experience with pandas for data manipulation
• Exposure to or experience with REST APIs and working with JSON
• Basic understanding of machine learning (e.g., scikit-learn, regression/classification)
• Experience with any dashboard or visualization tool (Power BI, Streamlit, Tableau, etc.)
• Familiarity with version control (e.g., Git)

Testing & Quality Responsibilities:

• Manual testing for web, mobile, and AI features; write clear test cases and bug reports.
• Automation for key flows (web/mobile/API) with pytest, Playwright/Cypress, Appium, Postman; keep a green regression suite.
• API & DB testing: validate responses/error handling; use SQL to check data accuracy, constraints, and migrations.
• Cross-browser/device checks plus basic usability & accessibility (WCAG); confirm analytics/telemetry events.
• E2E/regression runs for major user journeys; maintain smoke tests for quick health checks.
• LLM quality & safety: small eval sets; grounding, hallucination, toxicity/PII, prompt-injection; track via LangSmith/DeepEval/Ragas.
• Integration quality (HubSpot, Autotask): auth, pagination, rate limits, schema changes; validate pipeline SLAs.
• Release quality: light performance (k6/Lighthouse) and security (ZAP), CI test gates, feature-flag/canary rollouts, clean test data/observability.

Nice-to-Have Skills (Preferred but Not Mandatory)

• Exposure to LLM tools like OpenAI, LangChain, or HuggingFace
• Experience with FastAPI or Flask for lightweight backend services
• Understanding of RAG pipelines, vector databases (FAISS, Chroma), or prompt chaining
• Familiarity with Microsoft Fabric, Azure Synapse, or ADLS Gen2.
• Interest in LLM observability or evaluation tools like LangSmith, DeepEval, or Ragas