AI Platform and Harness Engineer

LTS

13d

About The Position

LTS is seeking an AI Platform and Harness Engineer to develop and maintain the infrastructure, tooling, and evaluation frameworks that power enterprise AI solutions. This role is responsible for building the AI platform and reusable "AI harnesses" that enable Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and Generative AI applications to be securely developed, tested, evaluated, monitored, and deployed at scale. The ideal candidate has experience with AI platforms, LLMOps, software engineering, cloud-native technologies, and backend systems, along with a passion for building reliable, observable, and production-ready AI solutions. You will work closely with AI architects, software engineers, data scientists, and product teams to ensure AI solutions are scalable, secure, cost-effective, and continuously improving.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field.
5+ years of experience in software engineering, platform engineering, backend engineering, DevOps, cloud engineering, or infrastructure engineering.
2+ years building or supporting Generative AI, Large Language Model (LLM), or machine learning applications.
Strong programming experience in Python.
Experience developing APIs, backend services, and distributed systems.
Experience with cloud platforms including AWS, Azure, or Google Cloud Platform.
Experience deploying applications using Docker and Kubernetes.
Experience working with Git, CI/CD pipelines, Infrastructure as Code (IaC), and infrastructure automation.
Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt engineering, Embeddings, Vector databases, AI agents and agentic workflows
Familiarity with AI evaluation techniques, automated testing, benchmarking, regression testing, and model validation.
Experience building scalable, production-grade software platforms.
Strong problem-solving, debugging, and performance optimization skills.

Nice To Haves

Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or AutoGen.
Experience implementing LLMOps or MLOps platforms and deployment pipelines.
Experience with AI observability tools such as LangSmith, OpenTelemetry, Prometheus, Grafana, Evidently AI, or Arize AI.
Experience with vector databases including Pinecone, Qdrant, Weaviate, Azure AI Search, or pgvector.
Experience with OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Google Vertex AI, or similar enterprise AI platforms.
Experience implementing Responsible AI, AI governance, model security, and AI safety best practices.
Experience supporting Federal Government or other regulated environments.
Experience evaluating AI systems for quality, reliability, accuracy, explainability, latency, and cost optimization.
Familiarity with healthcare, enterprise modernization, or mission-critical systems.

Responsibilities

Design, build, and maintain enterprise AI platform capabilities supporting Large Language Models (LLMs), AI agents, RAG, and Generative AI applications.
Develop reusable AI harnesses to automate testing, prompt evaluation, model benchmarking, regression testing, and quality assurance.
Build AI evaluation frameworks to measure model accuracy, retrieval quality, hallucination detection, latency, throughput, cost, and overall application performance.
Implement observability and monitoring solutions for AI applications, including telemetry, tracing, logging, dashboards, and operational metrics.
Build and maintain LLMOps pipelines supporting model deployment, versioning, evaluation, experimentation, rollback, and continuous improvement.
Design automated workflows for prompt testing, retrieval evaluation, AI system validation, and performance benchmarking.
Develop internal tools for prompt management, model experimentation, AI performance optimization, and developer productivity.
Build scalable backend services and APIs supporting AI platforms and enterprise AI integrations.
Collaborate with AI architects and engineering teams to integrate LLMs, RAG pipelines, vector databases, and agentic AI solutions into enterprise applications.
Support deployment of AI services across AWS, Azure, or Google Cloud using containerized and cloud-native architectures.
Implement CI/CD pipelines and infrastructure automation supporting enterprise AI development and deployment.
Apply security, governance, and Responsible AI controls throughout the AI development lifecycle.
Evaluate emerging AI frameworks, LLMOps technologies, evaluation methodologies, and automation tools to improve engineering productivity.
Troubleshoot production AI issues and continuously improve platform reliability, scalability, security, and user experience.
Document engineering standards, AI platform architecture, evaluation methodologies, and operational best practices.