Senior Full Stack Engineer

Anblicks

$130K — $155K *
Information Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 10+ years of full stack engineering experience delivering enterprise applications
  • Strong analytical, debugging, and problem-solving skills
  • Hands-on experience with Python, specifically in API development
  • Proficient in building scalable frontend applications and reusable UI components
  • Knowledge of AI agent orchestration and workflow automation
  • Experience with Agile methodologies, code reviews, and CI/CD practices

Responsibilities

  • Design and develop cloud-native services and APIs for the AI workflow
  • Build AI skills and orchestration flows to enhance software development processes
  • Integrate LLM services with various enterprise systems and CI/CD tools
  • Collaborate on implementing memory and workflow state capabilities
  • Write clean, maintainable code with a focus on testing and security
  • Troubleshoot issues related to integrations and agent executions
  • Support pilot implementations and validate end-to-end scenarios

Benefits

  • Collaborative work environment focused on innovative technologies
  • Opportunity to work with cutting-edge AI tools and platforms
  • Possibility for career growth in a leading tech company
  • Hands-on experience with modern tech stacks and agile practices
  • Flexible work arrangements supporting work-life balance
Full Job Description
Senior Full Stack Engineer - AI Platform & Agent Orchestration


Location Onsite USA | Dallas / Richardson, TX preferred Primary Focus Full stack development, AI workflow implementation, agent orchestration, integrations, and pilot delivery

Role Summary

We are seeking a Senior Full Stack Engineer with strong analytical and problem-solving skills to build AI-enabled SDLC platform capabilities. The engineer will develop backend services, user interfaces, agent orchestration flows, skills, integrations, automation pipelines, and pilot-ready features that support autonomous software delivery with human checkpoints.

Key Responsibilities
  • Design and develop cloud-native services, APIs, workflow interfaces, dashboards, and agent management capabilities.
  • Build and enhance AI skills, tools, agents, and orchestration flows for planning, design, coding, review, testing, and release of workflows.
  • Integrate LLM services with enterprise systems, repositories, CI/CD pipelines, Jira, Azure DevOps, GitHub, and internal knowledge sources.
  • Implement memory, retrieval, artifact management, audit trail, and workflow state capabilities in collaboration with the Technical Lead and AI Engineer.
  • Write clean, secure, testable, and maintainable code with automated testing and CI/CD practices.
  • Troubleshoot agent execution issues, API failures, integration defects, performance bottlenecks, and quality concerns.
  • Support pilot implementation, end-to-end scenario validation, demo preparation, telemetry capture, and backlog refinement.

Required Qualifications
  • 10+ years of full stack engineering experience delivering enterprise applications.
  • Strong analytical, debugging, and problem-solving mindset.
  • Hands-on Python engineering experience with API development and service integration.
  • Experience building scalable frontend applications and reusable UI components.
  • Working knowledge of AI agent orchestration, LLM APIs, tool calling, RAG concepts, prompt engineering, and workflow automation.
  • Experience with Agile delivery, code reviews, automated testing, CI/CD, and collaborative engineering practices.

Technical Skills
  • Backend: Python, FastAPI, REST APIs, microservices, event-driven integrations
  • Frontend: React.js, TypeScript, Next.js, modern UI frameworks, dashboards
  • Cloud/Ops: AWS, Docker, CI/CD, GitHub, GitHub Actions, Azure DevOps, logging, monitoring and deployment automation.

Preferred Qualifications
  • Experience with LangGraph, LangChain, CrewAI, or comparable workflow orchestration tools.
  • Experience with vector databases, embeddings, context retrieval, and memory-backed AI applications.
  • Exposure to AI-enabled developer productivity platforms, code assistants, and agentic SDLC patterns.
  • Ability to independently convert business and platform requirements into working features and integrations.

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