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Pod Network Jobs in Michigan (NOW HIRING)

Contribute independently within an FDE pod while mentoring newer team members. Solution Engineering ... AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for ...

Experience with Kubernetes (Cluster Orchestration, Container Networking, Pod Management) * Experience with DevOps, CI/CD, Deployment Orchestration (Octopus Deploy) * Experience with Git and Azure ...

Experience with Kubernetes (Cluster Orchestration, Container Networking, Pod Management) * Experience with DevOps, CI/CD, Deployment Orchestration (Octopus Deploy) * Experience with Git and Azure ...

Experience with Kubernetes (Cluster Orchestration, Container Networking, Pod Management) * Experience with DevOps, CI/CD, Deployment Orchestration (Octopus Deploy) * Experience with Git and Azure ...

Experience with Kubernetes (Cluster Orchestration, Container Networking, Pod Management) * Experience with DevOps, CI/CD, Deployment Orchestration (Octopus Deploy) * Experience with Git and Azure ...

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Pod Network information

See Michigan salary details

$12

$23

$33

How much do pod network jobs pay per hour?

As of Jun 13, 2026, the average hourly pay for pod network in Michigan is $23.22, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.34 per hour, depending on experience, location, and employer.

What are Pod Networks?

Pod Networks refer to the way networking is implemented for pods in a Kubernetes cluster. A pod network enables communication between pods across different nodes and ensures that each pod gets its own unique IP address within the cluster. These networks are typically managed by network plugins, also called Container Network Interfaces (CNIs), such as Calico, Flannel, or Weave. Proper configuration of a pod network is crucial for service discovery, load balancing, and secure communication between microservices within a Kubernetes environment.

What kind of jobs can you get with an EDD?

An EDD (Employment Development Department) certification or credential can qualify individuals for various employment opportunities depending on the specific program or training completed. Common jobs include administrative roles, technical positions, or skilled trades, often requiring relevant skills, certifications, or experience. The EDD itself is a state agency that provides unemployment benefits and job training resources, not a job title.

What are different jobs in education?

Jobs in education include roles such as teachers, school administrators, counselors, and educational coordinators. These positions often require relevant certifications, degrees, and skills in curriculum development, classroom management, and student assessment. Education professionals work in schools, colleges, and training centers, often following a structured schedule aligned with academic calendars.

What are the key skills and qualifications needed to thrive as a Network Engineer, and why are they important?

To thrive as a Network Engineer, you need a deep understanding of networking protocols, troubleshooting, and infrastructure design, typically supported by a degree in computer science or a related field. Proficiency with tools like Cisco IOS, Juniper, Wireshark, and relevant certifications such as CCNA or CCNP are highly valued. Strong problem-solving skills, attention to detail, and the ability to communicate complex issues clearly are important soft skills. These abilities ensure reliable network performance, swift issue resolution, and effective collaboration across IT teams.

What is the difference between Pod Network vs Network Engineer?

AspectPod NetworkNetwork Engineer
CredentialsCertifications like CCNA, CompTIA Network+Certifications like CCNA, CCNP, CompTIA Network+
Work EnvironmentContainerized environments, cloud platforms, KubernetesCorporate networks, data centers, enterprise infrastructure
Industry UsageDevOps, cloud computing, container orchestrationIT infrastructure, telecommunications, enterprise networking

Pod Network professionals focus on networking within containerized and cloud environments, often working with Kubernetes and cloud platforms. Network Engineers design, implement, and maintain traditional and enterprise networks. While both roles require networking certifications, Pod Network specialists are more involved with cloud-native and containerized environments, whereas Network Engineers work across broader physical and virtual networks in various industries.

What are some of the typical challenges faced by professionals managing Pod Networks in a cloud-native environment?

Professionals managing Pod Networks often encounter challenges such as ensuring secure communication between pods, maintaining network policies across dynamic environments, and troubleshooting connectivity issues that can arise from frequent scaling or updates. They also need to stay updated with evolving orchestration tools like Kubernetes, as networking plugins and configurations may change rapidly. Additionally, balancing performance optimization with network segmentation and security compliance requires ongoing collaboration with DevOps and security teams.

What does pod network stand for?

In the context of a Pod Network, which is relevant to container orchestration roles like Kubernetes administrators or network engineers, it refers to the virtual network that connects individual pods within a cluster. This network enables communication between pods and external systems, often managed through network plugins or CNI (Container Network Interface) tools. Understanding pod networking is essential for configuring secure, efficient, and scalable container environments.
What cities in Michigan are hiring for Pod Network jobs? Cities in Michigan with the most Pod Network job openings:
Infographic showing various Pod Network job openings in Michigan as of June 2026, with employment types broken down into 84% Full Time, and 16% Part Time. Highlights an 100% In-person job distribution, with an average salary of $48,303 per year, or $23.2 per hour.
Forward Deployed Engineer - Snowflake

Forward Deployed Engineer - Snowflake

Deloitte

Lansing, MI • On-site

Other

Posted 28 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

58th of 138 rated financial services


Job description

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/12/2026.

Work you'll do

As a Snowflake FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. Additional responsibilities include:

Client Engagement

  • Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.
  • Partner with leaders, product owners, architects, and engineers to align priorities and delivery.
  • Lead working sessions to shape solutions and drive client outcomes.
  • Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
  • Contribute independently within an FDE pod while mentoring newer team members.

Solution Engineering

  • Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  • Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  • Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  • Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
  • Design extensible functionality, support sprint sizing, and align solutions with senior team members.
  • Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.


 The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
  • 3+ years of experience in software engineering, data engineering, data science, or analytics engineering. 
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Snowflake including hands-on experience with one of the following key platforms; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $113,100 to $208,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Qualifications:

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/12/2026.

Work you'll do

As a Snowflake FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. Additional responsibilities include:

Client Engagement

  • Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.
  • Partner with leaders, product owners, architects, and engineers to align priorities and delivery.
  • Lead working sessions to shape solutions and drive client outcomes.
  • Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
  • Contribute independently within an FDE pod while mentoring newer team members.

Solution Engineering

  • Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  • Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  • Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  • Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
  • Design extensible functionality, support sprint sizing, and align solutions with senior team members.
  • Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.


 The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
  • 3+ years of experience in software engineering, data engineering, data science, or analytics engineering. 
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Snowflake including hands-on experience with one of the following key platforms; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $113,100 to $208,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Education:Bachelor's DegreeEmployment Type:

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