AI engineer

Job Description

W-2 Open Positions Need to be Filled Immediately. Consultant must be on our company payroll, Corp-to-Corp (C2C) is not allowed.

Candidates encouraged to apply directly using this portal. We do not accept resumes from other company/ third-party recruiters -->

Job Overview

Job ID: J52945

Posted Date: 01/21/2026

--> Specialized Area: other

--> Job Title: AI engineer

Location: dallas, TX

Duration: 10 Months + Extension

Hourly Rate: Depending on Experience (DOE)

Domain Exposure: Insurance

--> Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, L2-EAD, GC-EAD

Client: To Be Discussed Later

Employment Type: W-2, 1099, C2C Bench Recruiter: Blessy roy

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Role Summary

The candidate should be able to serve as the lead technical contributor for designing and deploying enterprise-grade AI systems. This role demands a senior AI engineer who can handle high-level architectural design and hands-on implementation of complex agentic workflows. The candidate will be responsible for building the "AIObserve" ecosystem, ensuring that probabilistic AI outputs are translated into deterministic, secure, and high-value business outcomes.

Core Responsibilities • Architecting Agentic Systems: Design and implement multi-agent systems using the Model Context Protocol (MCP) to enable seamless tool-calling across platforms like Atlassian and GitHub. • Enterprise RAG Implementation: Lead the development of sophisticated Retrieval-Augmented Generation (RAG) layers, integrating vector databases like Milvus with enterprise knowledge bases (Jira/Confluence). • Orchestration & Workflow Automation: Build and optimize backend services using FastAPI and Azure Bot Service to handle real-time message routing and automated ticket fulfillment. • High-Privilege Automation: Develop secure browser automation scripts using Python and Playwright to handle complex tasks such as RBAC validation and post-true-up process automation. • Security & RBAC Engineering : Engineer robust Role-Based Access Control (RBAC) within AI agents to ensure high-privilege operations are executed safely and within compliance. • Performance Tuning: Optimize system latency to ensure AI responses and backend acknowledgments meet strict enterprise thresholds (<7 seconds). • Architecting Observability Pipelines: Design and implement end-to-end telemetry for AI agents. This includes capturing not just system logs, but also LLM-specific traces (latency, token usage, and "hallucination" scores) to provide a 360-degree view of system health • LLMOps Infrastructure: Own the deployment lifecycle, including CI/CD for prompt engineering , automated testing of RAG retrieval accuracy, and monitoring for "model drift" in production. • Cross-functional Collaboration: Working with product managers, data scientists, and business stakeholders to translate needs into AI solutions.

Preferred Qualifications

• BS/Advanced degree in quantitative fields: Computer Science, Data Science, Engineering , Business Analytics, Math/Statistics, or a related field • 7+ years of experience in applied AI engineering or related role with 2+ years in agentic development, and/or with a combination of context/prompt engineering • Expert-level Python proficiency with emphasis on modular, object-oriented code, strict typing, and rigorous unit/integration testing for production • Experience with building both conversational agents and workflow agentic processes in production • Applied experience with multiple LLM stacks/frameworks (e.g., OpenAI, Claude, Gemini, RAG pipelines), and agent orchestration systems (e.g., LangGraph, AutoGen, CrewAI, or LangChain building collaborative autonomous and complex AI workflows • Demonstrated comfort with prompt design strategies (chain-of-thought, few-shot) and context window optimization to ensure high-quality LLM outputs • Familiarity with cloud platforms (AWS/Azure), REST APIs, and containerization (Docker, K8s) • Experience implementing and managing Vector Databases (e.g., Pinecone, Milvus, Weaviate) for RAG (Retrieval-Augmented Generation) pipelines. • Experience with Azure bot services, Fast API, OAuth for API security is recommended. • Proficiency in Databricks and SQL (DDL/DML) driving scalable data architecture and holistically integrating prompt designs, vector databases, and memory strategies to deliver advanced LLM solutions • Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost • Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production • Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

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