AI Engineer

Dallas, IA - USA

Monthly Salary: Not Disclosed

Posted on: 10 hours ago

Vacancies: 1 Vacancy

Job Summary

Role- AI Engineer

Location- Dallas TX (onsite)

Required Experience- 12 years

Must have Banking/Financial Domain Experience

Job Description

Build agentic AI systems: Design and implement tool-calling agents that combine retrieval structured reasoning and secure action execution (function calling change orchestration policy enforcement) following MCP protocol. Engineer robust guardrails for safety compliance and least-privilege access.
Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines prompt synthesis response validation and self-correction loops tailored to production operations.
Integrate with runtime ecosystems: Connect agents to observability incident management and deployment systems to enable automated diagnostics runbook execution remediation and post-incident summarization with full traceability.
Collaborate directly with users: Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability risk reduction and cost; and deliver auditable business-aligned outcomes.
Safety reliability and governance: Build validator models adversarial prompts and policy checks into the stack; enforce deterministic fallbacks circuit breakers and rollback strategies; instrument continuous evaluations for usefulness correctness and risk.
Scale and performance: Optimize cost and latency via prompt engineering context management caching model routing and distillation; leverage batching streaming and parallel tool-calls to meet stringent SLOs under real-world load.
Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
Raise the bar: Drive design reviews experiment rigor and high-quality engineering practices; mentor peers on agent architectures evaluation methodologies and safe deployment patterns

ESSENTIAL SKILLS

5 years of software development in one or more languages (Python C/C Go Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
3 years designing architecting testing and launching production ML systems including model deployment/serving evaluation and monitoring data processing pipelines and model fine-tuning workflows.
Practical experience with Large Language Models (LLMs): API integration prompt engineering fine-tuning/adaptation and building applications using RAG and tool-using agents (vector retrieval function calling secure tool execution).
Understanding of different LLMs both commercial and open source and their capabilities (e.g. OpenAI Gemini Llama Qwen Claude).
Solid grasp of applied statistics core ML concepts algorithms and data structures to deliver efficient and reliable solutions.
Strong analytical problem-solving ownership and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS) including containerized services (ECS/EKS) serverless (Lambda) data services (S3 DynamoDB Redshift) orchestration (Step Functions) model serving (SageMaker) and infra-as-code (Terraform/CloudFormation).

Role- AI Engineer Location- Dallas TX (onsite) Required Experience- 12 years Must have Banking/Financial Domain Experience Job Description Build agentic AI systems: Design and implement tool-calling agents that combine retrieval structured reasoning and secure action execution (function call...