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# AI Senior Engineer focused on LLMOps

- **Company:** Nesco Resource, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Application Firewall, Microsoft Azure, Cloud Computing, Cloud Engineering, Configuration Management, Cyber Security, Databases, Continuous Delivery, Continuous Integration, Data Infrastructure, Information Leak Prevention, Data Transformation, Data Security, Identity and Access Management, Python (Programming Language), Machine Learning, Mainframes, Microsoft SQL Server, Standard Sql, Azure Machine Learning, Search Technologies, Data Streaming, Systems Integration, Management of Software Versions, Workflow Management Systems, AI Infrastructure, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Snowflake, Model Validation, Multi-Cloud, Indexer, Cloudformation, AI Platforms, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Data Management, Machine Learning Operations, Terraform, Software Version Control, Automation Anywhere, Serverless Computing, Docker, Legacy Systems, Databricks
- **Published:** September 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-sr-engineer-llmops-mlops--0c552662-c18c-489d-afbc-7bb5f4b53a54

## About the Role

* Bachelor's degree in Computer Science or related field
 * 6+ years of engineering experience, including at least 3 years focused on MLOps or LLMOps in production
 * Strong hands-on expertise with both AWS and Azure AI ecosystems
 * Experience configuring services such as Amazon Bedrock, SageMaker, Azure AI Studio, and Azure OpenAI
 * Expert-level Python and SQL skills, with strong PySpark experience
 * Deep experience with Docker, Kubernetes, and orchestration tools such as Airflow, Kubeflow, or Step Functions
 * Experience building and supporting RAG pipelines, vector search, and semantic indexing
 * Familiarity with vector databases and search platforms such as OpenSearch, Pinecone, or Azure AI Search
 * Experience with LLM observability and evaluation tools such as LangSmith, Arize Phoenix, or WhyLabs
 * Strong understanding of model evaluation, validation metrics, and production monitoring
 * Experience using Terraform or CloudFormation for repeatable and secure infrastructure deployment
 * Ability to work effectively across Data Science, IT, Security, and enterprise platform teams

Preferred Skills or Experience

 * Master's degree in a quantitative discipline
 * Experience integrating with Databricks, Snowflake, or Palantir environments
 * Background supporting legacy data modernization in large enterprise environments
 * Experience with Bedrock Guardrails, Azure Content Safety, or similar AI security frameworks
 * Strong exposure to prompt versioning, model versioning, and production rollback strategies
 * Ability to operate with urgency in a transformation-focused environment while navigating enterprise governance, Amazon Web Services (AWS), Artificial Intelligence (AI), Channel Strategies, Cloud Computing, Computer Science, Computer Security, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Data Management, Data Modeling, Data Science, Docker, Ecosystems, Engineering, Enterprise Protection, Firewall Administration, Information/Data Security (InfoSec), Injections, Machine Learning, Metrics, Microsoft SQL Server, Microsoft Windows Azure, Model Validation, Production Control, Quality Management, Risk, Search Engine Optimization (SEO), Security Infrastructure, Semantic Search, Source Code/Configuration Management (SCM), Standards Development

## Description

Client is seeking an AI Senior Engineer focused on LLMOps and MLOps to own the production lifecycle of enterprise AI initiatives. This is a hands-on engineering role responsible for building the operational backbone that connects legacy data environments with modern AWS and Azure AI services. The ideal candidate will bring strong multi-cloud engineering experience and a proven ability to deploy, monitor, scale, and secure LLM applications, RAG pipelines, and traditional machine learning models in production.

Core Responsibilities

 * Build and maintain automated CI/CD and continuous training pipelines across AWS and Azure AI platforms
 * Design and operationalize Retrieval-Augmented Generation environments, including vector database integration and semantic search optimization
 * Engineer secure data pipelines from legacy systems such as mainframes, SQL Server, and on-prem databases into cloud-native AI workflows
 * Implement automated evaluation frameworks for LLMs and traditional ML models prior to production release
 * Establish monitoring for model drift, hallucination risk, latency, and token consumption to improve quality and manage cost
 * Manage AI infrastructure using Infrastructure as Code tools such as Terraform or CloudFormation
 * Partner with analytics and data platform teams to support reliable data flow between production models and platforms such as Databricks, Snowflake, or Palantir
 * Work closely with IT and Security teams on IAM, networking, firewall, and access configurations in a multi-cloud environment
 * Optimize model serving and inference endpoints for scale, resiliency, and performance using containers, Kubernetes, and serverless patterns
 * Create version control standards for prompts, model artifacts, and data snapshots to support auditability and rollback
 * Automate feature engineering, feature store workflows, and the transition from notebook-based experimentation into production services
 * Implement security guardrails and automated scanning to reduce prompt injection and data leakage risk


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