Senior AI Platform and Harness Engineer

LTS; United States; On-site; USD 164,000 - 200,000; Full time

Benefits offered by this job

Competitive salary

Comprehensive benefits

cutting-edge tools

Career development

Job summary

LTS is seeking a Senior AI Platform and Harness Engineer to build and maintain the infrastructure, tooling, and evaluation frameworks powering enterprise AI solutions. This role focuses on scalable backend services, reusable AI harnesses, and production-grade AI systems across LLMs, agents, and RAG pipelines.

The ideal candidate brings 7+ years in software or platform engineering, strong Python, cloud experience (AWS/Azure/GCP), Docker/Kubernetes, and a drive to deliver secure, observable AI at

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, AI, Data Science or related technical field.
• 7+ years of experience in software engineering, platform engineering, backend engineering, DevOps, cloud engineering or infrastructure engineering.
• 2+ years building or supporting Generative AI, LLMs, or ML 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 with Git, CI/CD pipelines, Infrastructure as Code (IaC) and infrastructure automation.
• Strong understanding of LLMs, RAG, Prompt engineering, embeddings, vector databases, AI agents and agentic workflows.
• Familiarity with AI evaluation techniques, automated testing, benchmarking, regression testing, and model validation.

Responsibilities

• Design, build, and maintain enterprise AI platform capabilities supporting LLMs, AI agents, RAG, and Generative AI apps.
• Develop reusable AI harnesses to automate testing, prompt evaluation, model benchmarking, regression testing, and QA.
• Build AI evaluation frameworks measuring model accuracy, retrieval quality, hallucination detection, latency, throughput, cost, and performance.
• Implement observability and monitoring for AI apps including telemetry, tracing, logging, dashboards and metrics.
• Build LLMOps pipelines for 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 into enterprise apps.
• Support deployment of AI services across AWS, Azure, or Google Cloud using containerized and cloud-native architectures.
• Implement CI/CD pipelines and infrastructure automation for enterprise AI development and deployment.
• Apply security, governance, and Responsible AI controls throughout the lifecycle.
• Evaluate emerging AI frameworks and automation tools to improve productivity.
• Troubleshoot production AI issues and improve platform reliability, scalability, security, and UX.
• Document engineering standards, AI platform architecture, evaluation methodologies, and best practices.

Skills

Python

LLMs

Cloud platforms

DevOps

Backend systems

CI/CD

IaC

Observability

Performance tuning

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

Git

Terraform

Job description

LTS is seeking a Senior 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.

What You'll Do:

• 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.

What We’re Looking For:

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field.
• 7+ 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 Have:

• 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.

What’s In It for You:

• The Opportunity to support high-visibility federal missions
• A culture that values innovation, growth, and collaboration
• Access to cutting-edge tools and technologies
• Comprehensive benefits for you and your family
• A career path that rewards ambition and performance

Pay Range

$164,000 — $200,000 USD

LTS shares salary ranges to promote transparency. Compensation ranges are provided for informational purposes, and final compensation may vary based on experience, skills, location, and role requirements.

LTSis committed to offering eligible employees comprehensive benefits that will provide them with options intended to meet their needs and the needs of their family.