Tech Lead AI Engineer

Position: Tech Lead – AI Engineering

Location: Dallas Texas (Hybrid)

Experince :Min 10+ years 

Position Overview

We are seeking an experienced Tech Lead – AI Engineering to lead the design, development, and production deployment of enterprise AI and generative AI solutions. The role requires strong hands-on expertise in Python, AWS, machine learning, large language models, API development, data integration, and event-driven architectures.

The Tech Lead will provide technical direction to AI engineers, data engineers, and software developers while collaborating with Southwest’s architecture, product, cybersecurity, cloud, and data teams. The ideal candidate must have delivered production-grade AI solutions—not only proofs of concept.

Key Responsibilities

●      Lead the architecture, design, development, and deployment of AI, machine learning, generative AI, and agentic AI solutions.

●      Translate business use cases into secure, scalable, and production-ready technical solutions.

●      Develop AI services, orchestration components, and backend applications using Python.

●      Design and implement generative AI solutions using large language models, prompt engineering, retrieval-augmented generation, embeddings, and vector databases.

●      Build AI agents and workflows capable of interacting securely with enterprise applications, APIs, databases, and business processes.

●      Develop and expose AI capabilities through RESTful APIs and microservices using FastAPI, Flask, or similar frameworks.

●      Integrate AI services with AWS platforms, Kafka-based event streams, enterprise data sources, and third-party applications.

●      Design data-ingestion and processing pipelines using AWS services such as S3, Lambda, Glue, ECS/Fargate, EventBridge, and API Gateway.

●      Establish appropriate model evaluation, monitoring, observability, auditability, and human-in-the-loop controls.

●      Implement safeguards for hallucination, prompt injection, sensitive-data exposure, bias, and inappropriate model responses.

●      Define reusable AI engineering standards, reference architectures, coding practices, and integration patterns.

●      Lead technical discovery, solution estimation, architecture reviews, code reviews, and design discussions.

●      Guide and mentor AI engineers, data engineers, and application developers.

●      Partner with cybersecurity and governance teams to ensure compliance with enterprise AI, privacy, and security requirements.

●      Support CI/CD, infrastructure automation, containerization, testing, production deployment, and incident resolution.

●      Communicate technical risks, dependencies, trade-offs, and recommendations to engineering and business leadership.

Required Technical Skills

●      Strong hands-on software-development experience using Python.

●      Experience leading the delivery of enterprise AI or machine-learning solutions in production.

●      Strong understanding of generative AI, large language models, prompt engineering, embeddings, retrieval-augmented generation, and AI agents.

●      Experience with AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.

●      Hands-on experience with AWS services, including Lambda, S3, API Gateway, ECS/Fargate, EventBridge, Glue, SQS/SNS, CloudWatch, and IAM.

●      Experience with Amazon Bedrock, SageMaker, or comparable enterprise AI platforms.

●      Strong knowledge of REST APIs, microservices, JSON, authentication, authorization, and enterprise application integration.

●      Experience with Apache Kafka or another event-streaming and messaging platform.

●      Strong understanding of SQL, data modeling, ETL/ELT pipelines, and relational or NoSQL databases.

●      Experience with vector databases or vector-search technologies.

●      Knowledge of OAuth 2.0, JWT, API keys, role-based access control, encryption, and secrets management.

●      Experience with Docker, Kubernetes or ECS, Git, and automated CI/CD pipelines.

●      Experience implementing unit, integration, API, model-evaluation, and performance testing.

●      Strong knowledge of logging, monitoring, tracing, error handling, retry mechanisms, and production support.