Full Stack Software Engineer (AI)

247Hire; Burbank (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

247Hire is building an AI-powered analytics platform for enterprise portfolio management. This role will design, build, and maintain reporting solutions that integrate data from our portfolio management system and multiple upstream systems, defining key metrics and delivering trusted insights for leadership.

This is a greenfield project focused on architecture, the semantic layer, and the AI pipeline, with supporting work in an existing Power BI environment.

Qualifications

• 5+ years of full-stack engineering experience with Python and/or TypeScript/JavaScript.
• Strong SQL and data warehouse experience with modern platforms.
• Hands-on experience with LLM APIs, prompt engineering, and summarization.
• Experience with RAG architecture, embeddings, and vector databases.

Responsibilities

• Design and build a text-to-SQL pipeline converting natural-language questions into safe SQL queries.
• Build a semantic layer defining table relationships, meanings, and business terminology.
• Integrate LLM providers for query generation, orchestration, and result summarization.
• Create automated dashboards and an analytical UI to visualize results and value metrics.

Skills

Full-stack engineering

Python

TypeScript/JavaScript

SQL & data warehouse

LLM APIs

RAG architecture

React/Next.js

CI/CD

Documentation

Architectural decisions

Education

Bachelor's degree in CS / related field

Tools

Snowflake

BigQuery

Redshift

Job description

Job Description – add details here

We are building an agentic natural-language query layer for enterprise portfolio management reporting. This platform will deliver standardized data, analytics, and visualizations for enterprise portfolio management reporting. It will enable business users to ask plain-English questions about technology projects, resources, timelines, budgets, and milestones and receive accurate answers, charts, and dashboards.

This role will design, build, and maintain reporting solutions that integrate data from our portfolio management system and multiple upstream systems, define key metrics, and provide trusted, actionable insights that enable leadership to make strategic, data-driven decisions.

This is a greenfield build focused on architecture, the semantic layer, and the AI pipeline. The role will also support an existing Power BI environment as a separate workstream. Clear technical and functional documentation will be critical to maintainability, adoption, and stakeholder confidence.

Responsibilities

• Design and build a text-to-SQL pipeline that converts natural-language questions into safe, permission-aware SQL queries.
• Build a semantic layer that defines table relationships, column meanings, and business terminology.
• Implement retrieval over schema documentation, business logic, and query examples to improve accuracy.
• Design multi-step agent workflows that can clarify requests, chain tool calls, and recover from query errors.
• Integrate LLM providers (OpenAI, Anthropic, etc.) for query generation, orchestration, and result summarization.
• Build guardrails for query validation, cost limits, row limits, and permission-based access.
• Create evaluation processes to measure query accuracy and detect unsupported outputs.
• Design and build a clear analytical UI for results, charts, and AI-generated summaries.
• Support the existing Power BI environment, including data models, DAX measures, and calculated tables.
• Design and maintain API gateways for governed data access across the warehouse, Power BI, and AI application.
• Integrate portfolio reporting data, project information, and health metrics from tools such as Smartsheet, Jira, Clarity PPM, ServiceNow, and SAP.
• Partner with data engineering to maintain clean, well-documented warehouse schemas.
• Own technical and functional documentation, including architecture, APIs, data dictionaries, workflows, and user guides.
• Partner with the Portfolio Management team and business stakeholders to define requirements and iterate on solutions.
• Design, build, and maintain automated dashboards that track performance metrics, identify historical trends, analyze forecast accuracy, and visualize business value outcomes to drive data-backed prioritization decisions.
• Support planning, prioritization, stakeholder communication, and delivery of high-quality code.

Basic Qualifications

• 5+ years of full-stack engineering experience with Python and/or TypeScript/JavaScript, including strong server-side and API design skills.
• Strong SQL and data warehouse experience, including schema design, joins, performance, and platforms such as Snowflake, BigQuery, or Redshift.
• Hands-on experience with LLM APIs, including prompt engineering, tool calling, structured outputs, and summarization.
• Practical experience with RAG architecture, embeddings, vector databases, and common failure modes.
• Front-end experience building analytical interfaces using React/Next.js and charting or data-grid libraries.
• Ability to work independently in ambiguous, 0-to-1 environments and document key architecture decisions.
• Strong technical and functional documentation skills.
• Experience designing governed API layers across multiple systems and consumers.
• Strong UI/UX judgment with a focus on clear, accessible, data-driven experiences.
• Solid engineering fundamentals, including testing, CI/CD, version control, code review, and performance optimization.
• Proven track record of developing comprehensive dashboards to monitor business performance, focusing on financial health, forecast-to-actual variance, trend analysis, pipeline prioritization, and strategic outcome value metrics.
• Excellent communication skills with the ability to translate complex technical concepts for varied audiences.
• Experience with LLM orchestration or agent frameworks such as LangGraph, CrewAI, AutoGen, or custom state-machine orchestration.
• Experience evaluating LLM output accuracy and identifying unsupported or hallucinated results.

Preferred Qualifications

• Power BI experience, including data models, DAX measures, and calculated tables.
• Experience with portfolio tools such as Smartsheet or Clarity PPM, and integrations with Jira, SAP, or ServiceNow.
• Familiarity with semantic layer tools such as dbt metrics, Cube, or LookML.
• Cloud and containerization experience using AWS, GCP, Azure, or Docker.
• Experience with caching layers such as Redis for latency-sensitive workloads.

Education

Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field of study, and/or equivalent work

Skills and Requirements – add details here

• 5+ years of full-stack engineering experience with Python and/or TypeScript/JavaScript, including strong server-side and API design skills.
• Strong SQL and data warehouse experience, including schema design, joins, performance, and platforms such as Snowflake, BigQuery, or Redshift.
• Hands-on experience with LLM APIs, including prompt engineering, tool calling, structured outputs, and summarization.
• Practical experience with RAG architecture, embeddings, vector databases, and common failure modes.
• Front-end experience building analytical interfaces using React/Next.js and charting or data-grid libraries.
• Ability to work independently in ambiguous, 0-to-1 environments and document key architecture decisions.
• Strong technical and functional documentation skills.
• Experience designing governed API layers across multiple systems and consumers.
• Strong UI/UX judgment with a focus on clear, accessible, data-driven experiences.
• Solid engineering fundamentals, including testing, CI/CD, version control, code review, and performance optimization.
• Proven track record of developing comprehensive dashboards to monitor business performance, focusing on financial health, forecast-to-actual variance, trend analysis, pipeline prioritization, and strategic outcome value metrics.
• Excellent communication skills with the ability to translate complex technical concepts for varied audiences.
• Experience with LLM orchestration or agent frameworks such as LangGraph, CrewAI, AutoGen, or custom state-machine orchestration.
• Experience evaluating LLM output accuracy and identifying unsupported or hallucinated results.