Third parties, absolutely no phone calls please .
Compensation-$ 40.00 per hour W2, $ 50.00 per hour C2C.
CANDDIATE MUST RESIDE LOCALLY TO THE DALLAS TEXAS REGION. The client requires a final onsite interview in Plano TX and is not open to canddiates who are willing to relocate.
Our client, a multinational telecommunications technology leader, is looking for mid-level AI Engineers to join their team in Plano, TX and help define how large language models are evaluated, monitored, and integrated into real-world systems.
This is a rare opportunity to work at the intersection of AI innovation and enterprise-scale infrastructure, with direct impact on how millions of users experience next-generation technology.
What You'll Do:
Build and maintain an LLM evaluation framework, including golden datasets, regression tests, scoring rubrics, quality thresholds, and trend tracking for non-deterministic outputs
Implement full observability for LLM pipelines: structured logging, metrics, dashboards, alerting, latency breakdowns, error taxonomies, and token/cost tracking
Design and integrate tool-calling and agent skills, including function calling, tool interfaces, input/output schemas, guardrails, and structured outputs into data pipelines and services
What You'll Gain:
Hands-on ownership of cutting-edge AI evaluation and observability systems
Exposure to large-scale, real-world AI infrastructure challenges
A collaborative environment with top engineers solving problems that have never been solved before
The chance to shape best practices for responsible, high-performing AI at a global scale
Required Skills:
Candidate must have 5+ years of hands-on experience in Software & Data Engineering and GenAI using LangGraph, LangChain, and Agentic design, development, and production deployment.
Experience productionizing AI/ML or LLM-powered workflows (using LangGraph, LangChain, CrewAI etc.) with a focus on reliability, reproducibility, and auditability
Strong understanding of LLMOps fundamentals, including prompt/model/config versioning and traceable run metadata.
Hands-on experience in Python, Pyspark/Spark, NO-SQL (MongoDB), SQL- PostgreSQL, Redis, Databricks, Delta Lake, Azure Cloud
Demonstrated curiosity and self-learning ability in fast-evolving GenAI industry; able to iterate quickly and then harden solutions for production
Experience with API reliability patterns relevant to model calls (rate limiting, retries/backoff, circuit breakers, timeouts, idempotency, replay/backfill).
Apply today for immediate considration. I look forward to hearing from you !!
