Owns the architecture and production delivery of enterprise data and AI solutions. Responsibilities include building data pipelines, governance integrations, semantic layers, AI agents, LLM applications, and readiness frameworks; applying technologies such as LangGraph, LangChain, RAG, and DataRobot; improving performance and scalability; and leading, mentoring, and managing a technical delivery team while engaging enterprise clients.
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 roleThis 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.
- 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.
- 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
- 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.
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