Full Stack Developer

Blend360•Columbia, MD

4d•$125,000 - $180,000

About The Position

Blend360 is a premier data, AI, and marketing consulting firm that partners with the world's most ambitious organizations to turn complex challenges into competitive advantage. We sit at the intersection of deep analytical rigor and pragmatic business execution—helping Fortune 1000 companies and Private Equity-backed businesses unlock transformational value through data, technology, and human expertise. Role summary Own architecture and delivery across multiple workstreams for Intelligent Applications. You’ll define “how we build” for forward deployed engagements—templates, standards, quality gates—and you’ll mentor and guide multiple engineers. You’ll bring observability/security to the forefront and operationalize LLM/agent delivery into repeatable, safe workflows (evals, guardrails, monitoring, human-in-the-loop where needed).

Requirements

6-8+ years equivalent experience or demonstrated staff-level impact.
Demonstrated multi-team or multi-stream technical leadership.
Strong full-stack architecture depth (React/Angular + Python services) and SQL performance leadership.
Strong best-practices governance mindset (systemic standards, not preferences).
Demonstrated leadership shipping production LLM systems (RAG, tool use, evaluations) and engineering governance around AI-assisted development (verification, secrets/PII, supply chain).

Nice To Haves

Experience operating in high-trust client environments and leading transformations.
Experience establishing agentic engineering guardrails and templates across teams.
Lakehouse/warehouse depth with Databricks and/or Snowflake (secure patterns, performance, operational integration with apps/APIs).

Responsibilities

Lead multiple streams (UI/API/data) with clear sequencing, risks, and integration plans.
Define reference architectures and standards: SOLID/MVC boundaries, error handling, testing, observability, security.
Drive performance and data architecture decisions: SQL/query patterns, indexing strategy, data contracts.
Create reusable accelerators: scaffolds, templates, spec/context packages, PR checklists, CI quality gates—including LLM/RAG reference modules and evaluation scaffolding.
Partner with data teams when engagements lean on Databricks or Snowflake for analytics/ML adjacency (contracts, identity, performance patterns).
Operationalize CI/CD and containerization standards: pipeline guardrails, environment parity, container-first dev workflows, and release verification patterns.
Coach senior + mid-level engineers; improve team throughput and quality.
Partner with leadership and clients to quantify impact and adoption.