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# AI/ML Engineer

- **Company:** Kforce Inc.
- **Location:** Maryland Heights, MO, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Amazon Web Services, Confluence, Cloud Computing, Computer Programming, Data Warehousing, Graph Database, Identity and Access Management, Python (Programming Language), Neo4j, Data Logging, Large Language Models, Apache Spark, Data Lakes, Apache Kafka, Data Management, Cloudwatch, Terraform, Dynatrace, Databricks
- **Published:** August 31, 2026
- **Apply:** https://www.dice.com/job-detail/1129c35b-e237-4e0d-856c-5b5e858839b5

## About the Role

* 3 years of experience in AI/ML & Cloud
 * Deep proficiency in AWS (IAM, CloudWatch, Bedrock, Lambda); Along with data warehouse (DWH) knowledge and building models on top of existing data platforms
 * Familiarity with the LLM lifecycle, including prompt execution, token usage, and frameworks like LangChain or AgentCore
 * Advanced experience with Terraform and CI/CD pipeline design
 * Experience working in an Agile environment with integrated tools like Microsoft Teams and Confluence
 * Strong programming background; Bedrock and AgentCore are technologies they are evaluating

Experience building systems utilizing:

 * RAG
 * LLMs
 * AWS

AI/ML Skills:

 * Some level of AI expertise required
 * Experience building agents
 * Ability to write MCPs
 * Experience orchestrating interactions between agents

Experience with:

 * Building on top of and calling LLM APIs
 * Graph databases: Neptune; Neo4j
 * SageMaker or similar platforms
 * Python or Java
 * Databricks or Data Lake solutions
 * Kafka and Spark are present, but the team already has support in those areas

## Description

Kforce has a client in Maryland Heights, MO that is seeking an AI/ML Engineer., * Observability: Assess CloudWatch, X-Ray, Bedrock logging, AgentCore traces vs. agentic workflow requirements; Produce gap analysis

 * Setup observability in Dynatrace
 * Design post-deployment validation pipeline for agents & MCP servers (deployment health + tool registration checks)
 * Implement distributed tracing & structured logging: LLM decisions, tool selections, sub-agent calls, MCP interactions
 * Evaluate LangFuse/LiteLLM proxy vs. AWS-native; Deliver target-state observability architecture recommendation
 * Cost Tracking & TCO: Extend tagging taxonomy to cover agent runtimes, MCP servers, vector DBs, Bedrock token consumption per namespace
 * Design cost visibility model: aggregate agent, MCP, vector DB, and Bedrock token costs per team/department
 * Build CloudWatch (or equivalent) dashboards for per-team spend; Configure AWS Budgets with alerting thresholds
 * Automate cost reports delivered via email
 * Connectivity issues: Integrate alert notifications to Microsoft Teams channels and email; Route by resource ownership tags
 * Author runbooks linked to every alert; Publish in Confluence for developer self-service resolution
 * Evaluate AWS-native vs. third-party monitoring stack; Deliver recommendation aligned to observability architecture
 * Security & Access Control: Assess current IAM + tagging approach for multi-team isolation; Identify scalability gaps and risks
 * Evaluate Cedar policy engine (AgentCore) for fine-grained tool access control


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