Python Developer

Net2Source (N2S)

Fort Worth, Arlington, Dallas (TX, TX, TX)

On-site

USD 100,000 - 130,000

Part time

14 days+
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Job summary

A technology solutions provider in Fort Worth, TX, seeks a Mid-Senior level engineer to develop advanced AI-driven workflows for code analysis and remediation. Ideal candidates should have over 5 years of experience, strong expertise in Python, and proficiency with AWS and observability tools. This contract role offers the opportunity to shape automated software modernization efforts across multiple engineering teams in a fast-paced environment.

Qualifications

  • 5+ years of experience building production-grade systems with end-to-end ownership.
  • Hands-on experience shipping LLM-powered features with measurable reliability or latency improvements.
  • Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code.

Responsibilities

  • Design, develop, and maintain LLM-powered workflows for code analysis and remediation.
  • Build scalable automation systems for technical debt remediation.
  • Collaborate to define engineering standards and reusable best practices.

Skills

Expertise in Python
Software engineering best practices
Experience with AWS
Knowledge of vector databases

Education

5+ years of experience building production-grade systems
Experience shipping LLM-powered features

Tools

PostgreSQL
Datadog

Job description

  • 5+ years of experience building production-grade systems with end-to-end ownership
  • Strong expertise in Python and software engineering best practices

Role Description:

Join a horizontal engineering team supporting 600+ application teams on a mission to elevate engineering maturity across the organization. This team drives standards, guidelines, platform capabilities, and large-scale technical debt remediation.

In this role, you will develop advanced agentic AI workflows to automatically analyze codebases, detect technical debt, and generate high-quality fixes—from vulnerability patches to dependency and language upgrades. This is a hands‑on, high‑impact opportunity to shape the future of automated software modernization.

Key Responsibilities:

  • Design, develop, and maintain LLM‑powered multi‑agent workflows for code analysis, remediation proposals, and safe patch generation
  • Implement agentic patterns such as planning/execution loops, tool orchestration, sandboxing, guardrails, and failure recovery
  • Build scalable automation systems for technical debt remediation, including language/runtime upgrades, dependency modernization, vulnerability patching, and configuration drift correction
  • Collaborate with Developer Experience and Platform teams to define engineering standards and reusable best practices
  • Architect and optimize RAG pipelines, including chunking strategies, embeddings, hybrid search, reranking, and retrieval policies
  • Develop evaluation frameworks for LLMs, RAG, and multi‑agent workflows, including offline datasets, validation metrics, statistical testing, and A/B experiments
  • Contribute to backend systems using Python, distributed systems, microservices, PostgreSQL, DBT, vector databases, caching, streaming, and queueing technologies
  • Build CI/CD pipelines, observability dashboards, and conduct performance analysis across model, retrieval, and network layers
  • Work cross‑functionally with product, platform, and security teams to take prototypes to production‑grade services
  • Communicate effectively with stakeholders, produce high‑quality technical documentation, and mentor junior engineers

Must‑Have Qualifications:

  • 5+ years of experience building production‑grade systems with end‑to‑end ownership
  • Expertise in Python, software engineering best practices, testing strategies, CI/CD, and system design
  • Hands‑on experience shipping LLM‑powered features (e.g., autonomous workflows, function calling) with measurable reliability or latency improvements
  • Strong understanding of multi‑agent architectures including planners, executors, and tool routing
  • Deep knowledge of RAG systems: chunking, embeddings, vector/hybrid search, retrieval policies
  • Experience evaluating LLMs and agent workflows using statistical reasoning and validation techniques
  • Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure‑as‑Code
  • Experience with observability tools (e.g., Datadog) covering logging, tracing, and metrics
  • Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning
  • Knowledge of vector databases such as Pinecone or pgvector
  • Experience designing or optimizing CI/CD pipelines (GitHub Actions or similar)
  • Proven track record in application modernization, dependency management, and technical debt reduction
  • Ability to rapidly prototype, validate, and transition solutions into production

Preferred Skills:

  • Experience designing agent infrastructure with sandboxing, tool isolation, and fail‑safe execution
  • Background in large‑scale platform engineering or developer experience tooling
  • Understanding of enterprise AI security, compliance, and privacy requirements
  • Strong architectural communication skills, including RFC development and technical diagramming

Attributes:

  • Adaptable, proactive problem solver
  • Strong ownership mindset with excellent collaboration and communication skills
  • Comfortable working in fast‑paced, ambiguous R&D environments
  • Passionate about building high‑leverage platform capabilities that support hundreds of engineering teams
Seniority level

Mid‑Senior level

Employment type

Contract

Job function

Information Technology

Industries

Banking, Telecommunications, and Information Services

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