AI Senior Engineer focused on LLMOps

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

• 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

Nesco Resource offers a comprehensive benefits package for our associates, which includes a MEC (Minimum Essential Coverage) plan that encompasses Medical, Vision, Dental, 401K, and EAP (Employee Assistance Program) services., Paid Sick Days, Parking, Performance Bonus, Employee Referral Program