Principal AI Architect, Tricentis
More information about location
Software testing platform
Be an early applicant
Software testing platform
1001+ employees
Be an early applicant
More information about location
1001+ employees
Company mission
To develop a comprehensive set of software testing tools to deliver end-to-end testing for DevOps and agile environments.
Role
Who you are
- 8+ years of experience as a senior, lead, or principal engineer or architect, including hands on experience with AI and ML systems
- Strong proficiency in programming languages commonly used in AI such as Python, Java, TypeScript or Node.js, or similar
- Expert-level proficiency in Python; experience with frameworks like LangGraph, LangChain, LlamaIndex, Semantic Kernel, or AutoGen
- Extensive experience with AI and ML technologies including large language models, embeddings, vector databases, RAG architectures, and model serving frameworks
- Hands on experience with Agentic AI patterns including autonomous agents, tool use, multi agent coordination, and goal directed action planning
- Strong background in cloud architectures across AWS, Azure, or GCP including containerization, serverless technologies, and distributed systems
- Experience designing secure AI systems with emphasis on data privacy, encryption, compliance, and responsible AI practices
- Solid understanding of API integration patterns, messaging systems, and event driven architectures
- Demonstrated ability to lead large scale architectural initiatives and influence cross functional technical decisions
- Excellent communication, problem solving, and technical leadership skills
- Hands on experience with modern architecture paradigms including microservices, domain driven design, and platform engineering
- Experience implementing scalable, distributed systems and high-performance AI pipelines
What the job involves
- Architect, design, and evolve a scalable and secure AI platform and reference architecture that enables rapid development of AI-powered product capabilities
- Drive strategic decision making across model selection, fine-tuning, retrieval augmented generation (RAG), agent frameworks, data pipelines, and inference optimization
- Evaluate and integrate open source and commercial LLMs, vector databases, feature stores, and MLOps platforms to accelerate AI adoption
- Collaborate with product management, engineering, architecture, and UX to define AI requirements, priorities, and architectural direction
- Lead and influence engineering teams through both direct and indirect contributions, fostering a culture of innovation and architectural excellence
- Participate in and lead architecture reviews, technical governance, and long-term platform planning
- Provide architectural direction for building Agentic AI systems, including workflow orchestration, multi-agent collaboration, context management, safety controls, and autonomous decision-making frameworks
- Design and implement LLMOps and AIOps practices for production agent systems
- Drive observability and AIOps practices for monitoring agent behavior and system performance
- Define guardrails for agent interactions, memory usage, and context boundaries to ensure safety and reliability
- Define and enforce target state and reference architectures, principles, guardrails, and standards for AI/ML, ensuring alignment with industry best practices, ethical frameworks (e.g., responsible AI), and regulatory guidelines (GDPR, NIST AI RMF)
- Establish processes for extracting metadata and implement metadata management for granular access control
- Define AI/ML technical capabilities—including generative AI systems, data pipelines, model deployment strategies, MLOps frameworks etc
- Define AI reference architecture, standards, and best practices
Our take
In a traditional software development cycle, developers would take months to create and deliver a product. Testing involved writing scripts to test all parts of the program, and when the software changed, the scripts often broke, requiring extensive, time-consuming manual fixing.
Tricentis has modernised this process, bringing software testing into line with today’s agile development environment, where programs are being updated more quickly and require faster testing. Tricentis' automated 'Continuous Testing' platform offers enterprises a streamlined way to ensure their software continues to perform as necessary in a fast-moving market.
Tricentis' solution is up to 10 times faster than manual testing and with a far lower error rate, leading to cost savings of up to 40%. This has built the company a solid customer base of over 2,000 companies, including major banks, insurance companies and telcos. Software testing and QA are estimated to be a $34 billion market, so Tricentis has the potential to become a major asset to any company engaged in software development. A staggering $1.33 billion investment from GTCR was awarded in 2024 and will no doubt aid Tricentis's growth.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
0% employee growth in 12 months
Company
Funding (2 rounds)
Nov 2024
$1.3bn
GROWTH EQUITY VC
Jan 2017
$176.1m
SERIES B
Total funding: $1.5bn
Company benefits
- Pension plan, Private health insurance and Group Life Insurance
- Enhance statutory Maternity and Paternity Pay
- A number of leave offerings: volunteer days, vacation days, public holidays, and flex/floating leave days
Company HQ
East Oak Hill, Austin, TX
Leadership
Lee McClendon
(CTO)Previously worked as the Senior Vice President of R&D at SolarWinds and WP Engine and was Technical Lead Developer at NetIQ.
Dave Hafner
(CFO)Was GVP of Finance at SolarWinds, CFO at Calabrio and VP of Financial Planning and Analysis at Workfront.
Kevin Thompson
(CEO)Previously CEO of SolarWinds, SVP and CFO at SAS and Red Hat.
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