R
Applied AI Engineer
RIT Solutions, Inc.
2 hours ago
Full-time
On-site
Broomfield, Colorado, United States
We are seeking an
Applied AI Engineer
to design, build, evaluate, and deploy AI-enabled solutions for business-critical applications in a consumer goods and manufacturing environment. This role will work within an enterprise Microsoft Azure architecture that includes API Management, Service Bus, Event Hubs, PostgreSQL databases, enterprise integrations, identity and security services, and connections to systems such as SAP, ordering platforms, and shop-floor management systems. The ideal candidate combines a strong foundation in
data science and machine learning
with hands-on experience building
production AI applications, AI agents, and enterprise AI integrations . Required Skills
Strong experience with
Python
for AI, machine learning, data science, and application development Strong background in
data science , including data preparation, feature engineering, statistical analysis, experimentation, and model evaluation Hands-on experience developing and deploying
machine learning models Experience with common ML frameworks and libraries such as:
PyTorch TensorFlow scikit-learn pandas NumPy
Strong experience developing
Generative AI and LLM-based applications Hands-on experience building
agentic AI solutions , including:
AI agents Tool/function calling Multi-step workflows Agent orchestration State and memory management Human-in-the-loop patterns Guardrails and controlled agent actions
Experience with
Retrieval-Augmented Generation (RAG) Experience with embeddings, semantic search, vector databases, and enterprise knowledge retrieval Strong understanding of
prompt engineering, context engineering, and structured model outputs Experience evaluating AI systems for:
Accuracy Reliability Hallucination Latency Cost Safety Business effectiveness
Fluent or professional working proficiency in
English
is required for written documentation, technical discussions, collaboration, and stakeholder communication. Enterprise AI Integration
Experience integrating AI solutions with
enterprise APIs, databases, messaging systems, and business applications Understanding of synchronous and asynchronous integration patterns Ability to design AI agents that securely interact with enterprise systems without providing unrestricted access Experience working with
structured and unstructured enterprise data Strong understanding of SQL, relational database structures, and application data models Experience with
PostgreSQL or similar relational databases Understanding of caching and state-management architectures for AI applications Knowledge of event-driven architectures and message-based processing Azure & Cloud Experience Experience building and deploying AI solutions within
Microsoft Azure , with familiarity with technologies such as:
Azure OpenAI Azure AI services and AI development tooling Azure API Management Azure Service Bus Azure Event Hubs Azure Database for PostgreSQL Microsoft Entra ID Identity and Access Management Azure Key Vault Azure Monitor and Application Insights Containers and cloud-native deployment CI/CD and automated deployment pipelines The candidate should be able to work effectively with cloud and platform engineering teams to move AI solutions from experimentation into
secure, scalable production environments . Machine Learning & Data Science Knowledge
Supervised and unsupervised learning Classification, regression, clustering, and ranking techniques Model selection and validation Feature engineering Statistical analysis and experimental design Model performance measurement Data quality assessment Model drift and production monitoring ML lifecycle and MLOps principles Ability to determine when traditional machine learning is more appropriate than an LLM-based solution AI Engineering & Agentic Development
Experience with AI orchestration frameworks or SDKs used to build agentic systems Understanding of
single-agent and multi-agent architectures Experience designing tool interfaces that allow AI systems to safely interact with APIs and enterprise applications Understanding of deterministic versus probabilistic application components Experience implementing
guardrails, validation, retries, approval workflows, and human oversight Knowledge of AI observability, tracing, evaluation, and debugging Understanding of token consumption, model latency, model selection, and AI cost optimization Experience designing AI systems that operate reliably within established business rules and constraints Industry & Business Experience
Experience developing AI, machine learning, analytics, or software solutions for
consumer goods, manufacturing, supply chain, distribution, or related industries Ability to understand business processes and translate operational problems into practical AI solutions Experience working with enterprise data such as:
Product data Order data Customer data Manufacturing data Supply-chain data Master data
Understanding of enterprise systems such as ERP, MES, WMS, order-management, or shop-floor platforms Architecture & Engineering Knowledge
Strong understanding of
REST APIs and enterprise integration patterns Familiarity with microservices and distributed application architectures Understanding of authentication, authorization, and secure access to enterprise data Knowledge of resiliency, fault handling, retry strategies, observability, and production support Ability to design AI solutions that can be integrated into existing enterprise applications rather than operating only as standalone prototypes Preferred Experience
Experience with
SAP, SAP S/4HANA, SAP BTP, SAP Integration Suite/CPI, SAP MDG, or SAP-related data Experience applying AI or machine learning to
manufacturing, product configuration, order management, forecasting, quality, supply chain, or operational processes Experience with
MLOps and LLMOps Experience with vector search platforms and AI knowledge-retrieval architectures Experience with distributed caching technologies such as Redis Experience with AI evaluation and observability frameworks Experience using AI-assisted software development tools such as
OpenAI Codex, Claude Code, or GitHub Copilot Experience taking AI solutions from proof of concept through
production deployment and operational support Key Characteristics
Strong analytical and problem-solving skills Comfortable working across data science, software engineering, and enterprise architecture Able to experiment rapidly while maintaining production engineering discipline Able to explain AI concepts, limitations, risks, and results to both technical and business stakeholders Strong focus on solving measurable business problems rather than implementing AI for its own sake
Applied AI Engineer
to design, build, evaluate, and deploy AI-enabled solutions for business-critical applications in a consumer goods and manufacturing environment. This role will work within an enterprise Microsoft Azure architecture that includes API Management, Service Bus, Event Hubs, PostgreSQL databases, enterprise integrations, identity and security services, and connections to systems such as SAP, ordering platforms, and shop-floor management systems. The ideal candidate combines a strong foundation in
data science and machine learning
with hands-on experience building
production AI applications, AI agents, and enterprise AI integrations . Required Skills
Strong experience with
Python
for AI, machine learning, data science, and application development Strong background in
data science , including data preparation, feature engineering, statistical analysis, experimentation, and model evaluation Hands-on experience developing and deploying
machine learning models Experience with common ML frameworks and libraries such as:
PyTorch TensorFlow scikit-learn pandas NumPy
Strong experience developing
Generative AI and LLM-based applications Hands-on experience building
agentic AI solutions , including:
AI agents Tool/function calling Multi-step workflows Agent orchestration State and memory management Human-in-the-loop patterns Guardrails and controlled agent actions
Experience with
Retrieval-Augmented Generation (RAG) Experience with embeddings, semantic search, vector databases, and enterprise knowledge retrieval Strong understanding of
prompt engineering, context engineering, and structured model outputs Experience evaluating AI systems for:
Accuracy Reliability Hallucination Latency Cost Safety Business effectiveness
Fluent or professional working proficiency in
English
is required for written documentation, technical discussions, collaboration, and stakeholder communication. Enterprise AI Integration
Experience integrating AI solutions with
enterprise APIs, databases, messaging systems, and business applications Understanding of synchronous and asynchronous integration patterns Ability to design AI agents that securely interact with enterprise systems without providing unrestricted access Experience working with
structured and unstructured enterprise data Strong understanding of SQL, relational database structures, and application data models Experience with
PostgreSQL or similar relational databases Understanding of caching and state-management architectures for AI applications Knowledge of event-driven architectures and message-based processing Azure & Cloud Experience Experience building and deploying AI solutions within
Microsoft Azure , with familiarity with technologies such as:
Azure OpenAI Azure AI services and AI development tooling Azure API Management Azure Service Bus Azure Event Hubs Azure Database for PostgreSQL Microsoft Entra ID Identity and Access Management Azure Key Vault Azure Monitor and Application Insights Containers and cloud-native deployment CI/CD and automated deployment pipelines The candidate should be able to work effectively with cloud and platform engineering teams to move AI solutions from experimentation into
secure, scalable production environments . Machine Learning & Data Science Knowledge
Supervised and unsupervised learning Classification, regression, clustering, and ranking techniques Model selection and validation Feature engineering Statistical analysis and experimental design Model performance measurement Data quality assessment Model drift and production monitoring ML lifecycle and MLOps principles Ability to determine when traditional machine learning is more appropriate than an LLM-based solution AI Engineering & Agentic Development
Experience with AI orchestration frameworks or SDKs used to build agentic systems Understanding of
single-agent and multi-agent architectures Experience designing tool interfaces that allow AI systems to safely interact with APIs and enterprise applications Understanding of deterministic versus probabilistic application components Experience implementing
guardrails, validation, retries, approval workflows, and human oversight Knowledge of AI observability, tracing, evaluation, and debugging Understanding of token consumption, model latency, model selection, and AI cost optimization Experience designing AI systems that operate reliably within established business rules and constraints Industry & Business Experience
Experience developing AI, machine learning, analytics, or software solutions for
consumer goods, manufacturing, supply chain, distribution, or related industries Ability to understand business processes and translate operational problems into practical AI solutions Experience working with enterprise data such as:
Product data Order data Customer data Manufacturing data Supply-chain data Master data
Understanding of enterprise systems such as ERP, MES, WMS, order-management, or shop-floor platforms Architecture & Engineering Knowledge
Strong understanding of
REST APIs and enterprise integration patterns Familiarity with microservices and distributed application architectures Understanding of authentication, authorization, and secure access to enterprise data Knowledge of resiliency, fault handling, retry strategies, observability, and production support Ability to design AI solutions that can be integrated into existing enterprise applications rather than operating only as standalone prototypes Preferred Experience
Experience with
SAP, SAP S/4HANA, SAP BTP, SAP Integration Suite/CPI, SAP MDG, or SAP-related data Experience applying AI or machine learning to
manufacturing, product configuration, order management, forecasting, quality, supply chain, or operational processes Experience with
MLOps and LLMOps Experience with vector search platforms and AI knowledge-retrieval architectures Experience with distributed caching technologies such as Redis Experience with AI evaluation and observability frameworks Experience using AI-assisted software development tools such as
OpenAI Codex, Claude Code, or GitHub Copilot Experience taking AI solutions from proof of concept through
production deployment and operational support Key Characteristics
Strong analytical and problem-solving skills Comfortable working across data science, software engineering, and enterprise architecture Able to experiment rapidly while maintaining production engineering discipline Able to explain AI concepts, limitations, risks, and results to both technical and business stakeholders Strong focus on solving measurable business problems rather than implementing AI for its own sake