AI Native Software Engineer - Production AI & Cloud

Tech Mirrors

Dallas (TX)

Hybrid

USD 140,000 - 190,000

Full time

14 days+

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Job summary

Tech Mirrors is seeking a hands-on AI Native Software Engineer to design, build, and deploy production‑grade AI‑driven systems in hybrid Dallas environments. You will work on agent‑based architectures, AI platform integration, and cloud‑native development to deliver scalable, reliable enterprise solutions.

The role emphasizes production‑level AI systems, orchestration, and modern tooling with cloud platforms, containers, and serverless patterns.

Qualifications

Bachelor's degree in Computer Science, Engineering, or related field or equivalent practical experience.
8–10+ years of professional software engineering experience with ownership of production systems.
3+ years building and deploying AI/LLM based systems in production (agents, RAG pipelines, orchestration).
Strong experience designing and delivering cloud native systems, including APIs, microservices, containers, and serverless or event‑driven architectures.
Proficiency in Python, Java, or comparable backend languages.
Hands on experience with CI/CD pipelines, infrastructure as code, and monitoring or observability tools.
Proven ability to deliver production quality code, including testing, debugging, performance tuning, and operational readiness.
Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar.
Experience designing multi agent or distributed AI systems.
Familiarity with multi‑model and multi‑provider AI architectures.
Experience integrating AI solutions into enterprise scale systems or platforms.
Demonstrated experience optimizing AI workloads for cost, performance, and latency.

Responsibilities

Design, implement, and maintain AI agent workflows, including retrieval augmented generation (RAG), orchestration, tool/function invocation, and policy‑based routing.
Build cloud native backend services and APIs to support AI driven applications and enterprise integrations.
Implement evaluation, monitoring, and observability frameworks to ensure accuracy, latency, reliability, and system health across AI agent lifecycles.
Optimize AI and system performance across cost, scalability, and latency dimensions in production environments.