Typical Day in the Role
• Purpose of the Team: The purpose of this team is to take industrial controls data, refine and standardize it, and push it to the cloud so partner teams can use it for automation, reporting, predictive analytics, and other business needs.
• Key projects: This role will contribute to improving process efficiency through automation, maintaining and enhancing legacy systems, and developing solutions that support the modernization of industrial controls and telemetry data processes
Candidate Requirements
• Best vs. Average: The ideal resume would contain experience with industrial control systems, automation development, strong AI and machine learning capabilities, process improvement experience, and a demonstrated willingness to challenge and improve existing processes.
Job Description Summary:
The main function of a software engineer is to apply the principles of computer science and mathematical analysis to the design, development, testing, and evaluation of the software and systems that make computers work. A typical software engineer researches, designs, develops and tests operating systems-level software, compilers, and network distribution software for medical, industrial, military, communications, aerospace, business, scientific and general computing applications.
Job Responsibilities:
• Modify existing databases and database management systems.
• Write and code logical and physical database descriptions and specify identifiers of database to management system or direct others in coding descriptions.
• Work as part of a project team to coordinate database development and determine project scope and limitations.
• Review project requests describing database user needs to estimate time and cost required to accomplish project.
Skills:
• Verbal and written communication skills, problem solving skills, customer service and interpersonal skills.
• Ability to work independently and manage one''s time.
• Basic mentoring skills necessary to provide support and constructive performance feedback.
• Knowledge of the full software development lifecycle: from business/systems analysis, through requirements gathering and functional specification authoring, to development, testing and delivery.
• Ability to troubleshoot issues and make system changes as needed to resolve issue.
• Knowledge of computer hardware and software.
• Knowledge of computer development software as it relates to systems, such as SQL, VisualBasic, etc.
Education/Experience:
• Bachelor''s degree in computer science, software engineering or relevant field required.
• 5-7 years'' experience required.
Additional Experience:
Explain a typical day in the role.:
In a typical day, this engineer builds and maintains C#/.NET automation tooling for our telemetry onboarding workflow — extending console apps and Worker Services that turn Excel-based device templates into the CSV/config outputs our pipeline depends on. A core focus is applied machine learning: training and refining a classification model that predicts which telemetry tag maps to which template field, using years of historical taglists so the automation can generate a near-complete template with minimal human cleanup. They own the full lifecycle — requirements, development, testing, and delivery — working across Azure IoT, Event Hub, and Kusto/KQL to validate clean, correctly mapped data, troubleshooting and resolving issues rather than handing them off. They also contribute to the team''s AI-enablement direction (Copilot/MCP-style agent patterns), document their work, and mentor teammates to keep everything maintainable and on a single C#/.NET stack
What is the ideal background of a candidate for this role?:
The ideal candidate is a mid-to-senior software engineer (5–7 years, CS or related degree) who is software-engineering-led with strong applied-ML skills — someone who builds and ships tooling first, with machine learning as a meaningful component rather than their entire job. They''re fluent in C#/.NET (ideally .NET 10) and Visual Studio, building console apps and Worker Services that turn templates into config outputs, and they''re comfortable doing applied classification work (ML.NET, scikit-learn, or embeddings) for problems like tag-to-template mapping, including feature engineering on messy historical data. They prototype quickly in Python, then port production logic into C#/.NET to keep everything on a single stack, and they have enough Azure IoT, Event Hub, and Kusto/KQL fluency to validate telemetry data end-to-end while owning the full lifecycle from requirements through testing and delivery. Ideally they bring light MLOps ability (deploy/retrain within ADO/Azure without a dedicated specialist) and Copilot/agent (MCP) familiarity, and they''re a strong communicator and mentor who documents their work and helps teammates adopt new tooling — independent, self-managing, and oriented toward hardening prototypes into production rather than leaving them as scripts.
What are unique selling points that would get candidates interested in your role over another?:
Build the AI-powered automation that modernizes telemetry across companies global datacenter fleet — own a real applied-ML problem end-to-end on a modern C#/.NET stack, while helping shape the next-generation first-party platform.
How will contractor performance be measured?:
Contractor performance will be measured primarily on automation impact — quantified reductions in manual onboarding effort, such as cutting site integration from days to hours and datapoint deployment from hours to minutes. For the applied-ML work, success is measured by classification accuracy and coverage — the percentage of telemetry tags correctly mapped to template fields and the amount of human cleanup eliminated, with the goal of producing a near-complete initial template. Standard engineering measures also apply: on-time delivery of ADO work items, code quality, test coverage, and successful full-lifecycle handoffs (requirements through delivery). Tooling will be evaluated on production reliability and maintainability — fewer manual interventions, clean end-to-end telemetry, and code that stays within the team''s C#/.NET stack without adding tool sprawl. Finally, as a contractor, documentation, knowledge transfer, and the ability to work independently will be assessed to ensure the team can maintain all delivered tooling after the engagement ends.
Where is the work able to be performed?: Remote
If Onsite or HWA required, indicate why:
If REMOTE: Are work hours flexible to accommodate time zone variance (+/- 2-3 hours?): Yes
Top 3 Must-Have HARD Skills & years of experience for each:
1. C#/.NET Software Development — 5+ years
The core of the role is building and shipping tooling (console apps, Worker Services) that turn device templates into config/CSV outputs, so deep C#/.NET (ideally .NET 10) and Visual Studio proficiency is non-negotiable. This is the skill that keeps everything on the team''s single stack and ensures production-ready, maintainable code.
2. Applied Machine Learning / Classification — 3+ years
Hands-on experience building text/tabular classification models (ML.NET, scikit-learn, or embedding-based approaches) is essential for the tag-to-template mapping problem, including feature engineering on messy historical taglists and evaluating model accuracy. Python prototyping experience pairs naturally here before porting logic into C#.
3. Data-Plane / Telemetry Engineering (Azure IoT, Kusto/KQL, Event Hub) — 3+ years
The engineer must validate that telemetry data is clean and correctly mapped end-to-end, so practical fluency with Azure IoT, Event Hub, and Kusto/KQL is required to work confidently in the data environment the tooling feeds.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: talmn001
- Position Id: 26-07756
- Posted 2 hours ago
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