An extraordinarily talented group of individuals work together every day to drive TNS' success, from both professional and personal perspectives. Come join the excellence!
OverviewResearches, designs, develops, and operates secure, production AI platform capabilities by translating business objectives and departmental requirements into deployed AI solutions. Work typically includes assessing lines of business’ automation needs, building the data connectivity, security, and cost-management infrastructure that lets AI work safely against company data, and engineering the model hosting, orchestration, and monitoring systems that deliver measurable application value.ResponsibilitiesWhy this role existsTNS is moving from AI governance and foundations into execution. The platform building blocks are in place or underway — Microsoft 365, Copilot, Entra ID, SharePoint, and Anthropic's Claude models via AWS Bedrock. What we don't yet have is a dedicated person who can both build and operate the secure, production-grade AI platform that lets AI work safely against our data and embed with our lines of business to turn that capability into measurable value.
This role does both. You'll be the technical engine and the on-the-ground partner who makes AI a productive, trusted tool across the enterprise — and who multiplies that impact by enabling others to build, not just building yourself.
What you'll doYour time will span two connected halves.
Build and operate the AI platformModel platform and private hosting. Own our Claude deployment on AWS Bedrock end to end — model access and versioning, region and data-residency decisions, provisioned throughput vs. on-demand, and the private, VPC-isolated hosting posture that keeps our data inside our boundary and out of third-party training. Evaluate and manage additional models where they fit, and maintain a routing layer so applications aren't hard-wired to a single model or provider.
Harness and orchestration management. Build and maintain the application harness that sits between our users and the models: prompt and context management, tool and function calling, agent orchestration, retrieval pipelines, guardrails and content filtering, caching, and fallback behavior. Own the evaluation harness too — regression suites, quality benchmarks, and the release process that lets us upgrade models without breaking what's in production.
Data connectivity. Design and build secure, governed connections between AI systems and TNS data — Microsoft 365 and Graph, SharePoint, databases, data warehouses, APIs, and line-of-business systems. Own the retrieval architecture, indexing and freshness strategy, and the permission-trimming that ensures every user sees only what they're entitled to see, with no leakage across boundaries.
Security and access control. Architect least-privilege access across identity, network, and data layers — IAM and Entra ID, private networking, encryption and key management, secrets handling, and tenant isolation. Defend against AI-specific threats: prompt injection, data exfiltration through model outputs, insecure tool use, and over-permissioned agents. Partner with security and governance so every deployment is auditable and compliant by design, with a full audit trail of prompts, outputs, and data access.
Monitoring and observability. Instrument the platform for production operations — latency, error rates, throughput and quota headroom, output quality and drift, guardrail trips, and anomalous usage. Build the dashboards and alerting that let us find problems before users do, and the usage telemetry that tells us which solutions are actually delivering value.
Cost management. Own AI spend as a first-class engineering concern: token-level cost attribution by application and line of business, budgets and alerting, model right-sizing (using the cheapest model that meets the quality bar), prompt caching and context optimization, batch processing where latency allows, and provisioned-throughput decisions. Produce the chargeback and unit-economics reporting leadership needs to fund the portfolio with confidence.
Reusable foundations. Turn all of the above into patterns, templates, and guardrails so each new use case is faster, cheaper, and safer to build than the last.
Ship value with the business (forward-deployed engineering)Embed directly with teams across our lines of business (Communications, Waypoint, Payments, Corporate/Shared) to identify high-value AI and automation opportunities.
Prototype quickly, validate with real users, and take the strongest ideas to production.
Drive adoption — measure whether solutions are actually used and delivering value, and iterate until they are.
Enable others: seed and support a network of "AI champions" in each line of business, equipping non-specialists to build their own automations on your patterns and under appropriate oversight.
What you'll accomplish in your first year
Stand up a secure, monitored, cost-managed AI platform on AWS Bedrock with governed connectivity to TNS data.
Ship a portfolio of production use cases with documented, measurable value — reduced cost to serve, shorter cycle times, and volume absorbed without added headcount.
Establish a use-case intake and prioritization process so demand is triaged by value rather than by who asks loudest.
Deliver AI cost transparency and attribution by application and line of business.
Enable at least one capable AI champion in each line of business.
Strong software / solutions engineering background with hands-on experience building and deploying production AI/LLM applications — RAG, agents, tool use, orchestration, and evaluation.
Direct experience with a managed model platform in production; AWS Bedrock strongly preferred, along with the surrounding AWS estate (IAM, VPC, KMS, CloudWatch, PrivateLink).
Demonstrated ability to build and operate data connectivity to enterprise systems with permission-aware retrieval.
Working command of enterprise security and data governance: identity and access, data protection, encryption and key management, and AI-specific threat models.
Production operations instincts — you instrument what you build, and you've owned the monitoring and on-call reality of a live system.
Cost discipline: you treat inference spend as an engineering constraint and can show where the money goes.
Genuine business fluency and communication skills — you're comfortable sitting with finance, operations, or sales, understanding their problems, and translating them into solutions.
A demonstrated bias toward shipping and adoption, not just building demos.
Depth in the Microsoft / Azure ecosystem alongside AWS (Azure AI, Copilot Studio, Power Platform, Entra ID, SharePoint / Graph) — our environment spans both.
Experience in a regulated or security-sensitive industry — payments, telecom, or financial data.
Self-hosted or fine-tuned model experience, and a clear view of when it beats a managed API.
Infrastructure-as-code and CI/CD for AI systems (Terraform, pipelines, automated evals in the release path).
Change-management or internal-consulting experience.
Prior forward-deployed or customer-facing engineering experience.
The rare blend of builder and partner. You carry the technical depth of a staff engineer and the people instincts of an internal consultant. You're energized by variety — a private-networking and key-management problem in the morning, a finance-team workflow in the afternoon. You measure yourself by value delivered and adopted, not lines of code. You teach and multiply rather than hoard knowledge. And you operate with ownership: when a deployment is struggling or a cost-line spikes, you own the fix.
How your success is measuredIn short: reduced cost to serve on named processes, use cases shipped and sustained in production, adoption across lines of business, champions enabled, platform reliability and cost-per-unit-of-value, and a clean security and governance record.
For this role, we anticipate paying $150,000 - $250,000 annually. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate’s knowledge, skills, abilities, and geographic location. TNS offers a competitive benefit package including medical and dental coverage, life insurance, paid holidays and vacations, and a 401K plan with company match.
If you are passionate about technology, love personal growth and opportunity, come see what TNS is all about!
TNS is an equal opportunity employer. TNS evaluates qualified applicants without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, protected veteran status, disability/handicap status or any other legally protected characteristic.
Skills Required
- Strong software or solutions engineering background with hands-on experience building and deploying production AI or LLM applications, including RAG, agents, tool use, orchestration, and evaluation.
- Direct production experience with a managed model platform; AWS Bedrock is strongly preferred.
- Experience with AWS services including IAM, VPC, KMS, CloudWatch, and PrivateLink.
- Experience building and operating data connectivity to enterprise systems with permission-aware retrieval.
- Working knowledge of enterprise security and data governance, including identity and access, data protection, encryption, key management, and AI-specific threat models.
- Production operations experience, including monitoring, instrumentation, and live-system on-call ownership.
- Ability to manage inference costs through attribution, optimization, budgeting, and model right-sizing.
- Business fluency and communication skills, including translating business problems into technical solutions.
- Demonstrated bias toward shipping production solutions and driving user adoption.
- Experience with the Microsoft and Azure ecosystem, including Azure AI, Copilot Studio, Power Platform, Entra ID, SharePoint, or Graph.
- Experience in a regulated or security-sensitive industry such as payments, telecom, or financial data.
- Self-hosted or fine-tuned model experience.
- Infrastructure-as-code and CI/CD experience for AI systems, including Terraform, pipelines, or automated evaluations.
- Change-management or internal-consulting experience.
- Prior forward-deployed or customer-facing engineering experience.
Transaction Network Services Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Transaction Network Services and has not been reviewed or approved by Transaction Network Services.
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Leave & Time Off Breadth — Time off is considered a relative strength, with excellent PTO highlighted and increases after five years. Flexibility around taking time away from work supports a better overall balance for many roles.
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Flexible Benefits — Remote work options and flexibility are frequently highlighted alongside compensation and benefits, improving work-life fit for some teams. This flexibility helps offset more mixed views on other elements of the package.
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Healthcare Strength — Core medical, dental, and vision coverage are in place and are described as solid or subsidized in some contexts. Benefits are presented as competitive, reinforcing a stable baseline of health coverage.
Transaction Network Services Insights
What We Do
Transaction Network Services (TNS) is a leading global provider of data communications and interoperability solutions. Our global network and innovative value-added services enable transactions and the exchange of information to many of the world’s leading retailers, banks, payment processors, financial institutions and telecommunication firms. Founded in 1990 in the United States, TNS has grown steadily and now provides services to customers in over 60 countries across the Americas, Europe and the Asia Pacific region, with our reach extending to many more. TNS has designed and implemented a global data network which supports a variety of widely accepted communications protocols and is designed to be scalable and accessible by multiple methods.
