Experience: 5+ years
About the Job About Aligned Automation At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future. Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace. Role type: Hands-on technical lead for architecture, development and team delivery About the role This role owns the architecture and delivery of data and AI solutions that make enterprise data trustworthy and usable by AI. It combines solution architecture, hands-on data and AI engineering, and leadership of a multi-member delivery team. Key responsibilities Architecture: Own the end-to-end architecture of data and AI solutions, including the UI/UX and backend services of the applications built on them. Standards: Define data product and data contract standards aligned with data mesh principles, adopting open standards such as ODPS and ODCS. AI agents: Build AI agents and LLM applications using open-source frameworks, protocols and patterns such as LangGraph, LangChain, the Agent2Agent (A2A) protocol, Model Context Protocol (MCP) servers and retrieval-augmented generation (RAG). Agent engineering: Deliver reliable structured output, tune prompts, measure agent quality, and deploy and run agents at scale using DataRobot. AI readiness: Design explainable readiness frameworks with clear dimensions, rules, scoring and remediation guidance. Semantic layer: Understand and build ontologies, knowledge graphs, glossaries and taxonomies aligned to W3C DCAT and PROV-O. Data engineering: Build data models, pipelines and governance integrations, and enforce quality and lineage standards. Performance: Diagnose and fix performance and scalability bottlenecks across data and AI pipelines within agreed SLAs. Leadership: Lead and mentor the team, plan capacity, review designs and code, and own delivery to production. Stakeholders: Turn requirements into an architecture and a backlog, and present points of view and designs to clients. Skills AI-led development: UI/UX development, backend engineering, agent development and troubleshooting Data engineering: Python, SQL, PostgreSQL, SQL Server, semi-structured data, data modeling, pipelines, GitLab, CI/CD AI and LLM: Agent design, prompt engineering, structured outputs and LLM evaluation Data governance: Data mesh architecture Semantic technologies: Ontologies, knowledge graphs, RDF, DCAT, PROV-O, taxonomies and glossaries Integration and security: Secrets management (for example, HashiCorp Vault), security scanning (Snyk), code quality (SonarQube) and observability (Dynatrace) Leadership: Team leadership, delivery management, capacity planning, stakeholder management and executive presentations Qualifications 14 years of experience in data engineering, including hands-on work building AI agents and LLM applications. Proven record of architecting enterprise data solutions and leading teams to production. Degree in computer science, engineering or a related field, or equivalent experience.
