Senior Software Engineer - Conversational AI (Python)

Description

About Glia

Glia is the 1 Banking AI platform, empowering community and regional financial institutions to create efficiencies, accelerate loan growth, drive deposits, and deliver experiences that win against megabanks and fintechs.

Glia's Banking AI Operating System is a central intelligence layer on top of existing tech stacks, activating an AI workforce of specialized agents that draw from banking data, interaction history, and integrated systems of record. These banking-trained agents automate workflows across voice and digital–from front office to back office–resulting in decreased operational costs and the Universal Banker model.

Trusted by 700+ banks and credit unions for its ironclad security and reliability, Glia delivers the industry’s first contractual no-hallucination guarantee. It’s why Glia customers quickly and confidently put Banking AI to work with measurable results from day one. More information about Glia can be found at glia.com .

Job Title: Senior Software Engineer

Responsibilities

• Design, build, and ship the core of our agentic voice AI framework, from conversation orchestration to tool calling and runtime architecture.
• Build and evolve the harness controls (guardrails, grounding, policy checks, evaluation) that make our LLM agents trustworthy enough for banking.
• Work on latency end to end: profile, measure, and optimize every stage of the real-time voice pipeline.
• Help unify our runtime architecture and remove configuration friction so new banking use cases can launch quickly.
• Keep our existing production systems stable and reliable while we build what comes next.
• Work with product, design, and engineering teams across Europe and Vancouver to turn ambiguous problems into shipped outcomes.
• Use AI-assisted development tools daily and help set the standards for how we review and verify agent-generated code.
• Our Tech Stack:
• Coding languages: Python, Elixir, TypeScript, React (JavaScript)
• Persistence: Amazon RDS for PostgreSQL, DynamoDB, S3
• Infrastructure: AWS
• Monitoring: DataDog
• CI/CD: Jenkins, Argo CD
• AI: Gemini, Claude Code
• Infrastructure as Code: Terraform
• Other: Kafka, GitHub, Kubernetes, Docker

Requirements

• AI Systems Experience: A core requirement. You have built production AI systems before and you understand LLM architecture well: prompting and context management, tool use and agentic flows, retrieval and grounding, evaluation, and the tradeoffs between latency, cost, and quality. Real-time or voice AI experience is a strong plus, and so is experience with agent SDKs (such as the Claude Agent SDK or OpenAI Agents SDK).
• Architecture & Technical Judgment: You own the design of complex components, make sound tradeoffs, and spot risk early. When AI writes more of the code, system design becomes the scarce and highly valued skill.
• Excellent: Trusted, Fast Delivery: You ship fast without cutting corners. You have a track record of reliable outcomes, de-risking projects, and owning quality, including verifying and standing behind agent-generated code.
• Applied AI Fluency: You actively use tools like Claude to design, build, review, and ship. You can direct agents, verify their output, and you know where they help and where they don’t.
• Proactive: You see problems before they’re assigned to you and act on them. You partner with PMs to take projects from idea to delivery, and you raise issues, propose solutions, and drive them forward without waiting to be asked.
• Adaptable: You do well in a fast-paced environment where priorities, tools, and the AI landscape itself change quickly. You adjust without losing momentum.
• Curious: Learning Velocity: You’re hungry to learn. You run at unfamiliar problems, pick up new tools and practices quickly, and share what you learn with the people around you.
• Cross-Continental Collaboration: You communicate clearly in writing and in person, and you do well on a distributed team. You can reliably overlap at least 2 hours a day with Pacific Time (9:00–11:00 AM PT) to work closely with teammates in Vancouver.
• Leadership Instincts : You set direction, unblock people, and raise the bar without micromanaging. Ideally, you bring prior tech lead, team lead, or mentorship experience
• Glia is an equal-opportunity employer. Glia does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law.
• The Glia Talent Acquisition team uses @ glia.com and @ gliatalent.com email addresses for coordinating interviews, providing updates, and sending documents.

Conditions

Meet Team Vector:

Team Vector builds the AI that talks directly to the customers of banks and credit unions. When someone calls their credit union to check a balance, dispute a charge, or ask about a loan, it’s our agent on the other end of the line.

Our vision: To build the world’s most intuitive and trusted customer-facing banking AI agents

Our mission: To build and scale a fully agentic voice AI framework that drives high-value customer interactions. We unify our runtime architecture, remove configuration friction, and keep our existing systems fully stable so customers never see a disruption.

Why This Work Is Interesting:

Voice AI with very low latency. People notice every pause in a conversation. We aim for the best voice AI experience available, which means working to shave milliseconds off every hop: speech recognition, LLM inference, tool calls, and speech synthesis. Latency is a first-class engineering constraint for us, not an afterthought.

LLM architecture under real constraints. Banking is one of the most regulated places to put an LLM in front of a customer. Every response has to be safe, accurate, compliant, and on-brand, and it still has to feel natural. That calls for a detailed harness around the model: guardrails, grounding, policy enforcement, verification layers, and controlled agentic flows. Building those controls without giving up speed or conversational quality is one of the hardest and most interesting problems in applied AI right now.

Real impact at scale. The agents you build will handle real conversations for real people across hundreds of financial institutions. What you ship matters.

A global team. Team Vector works across Europe and Vancouver, Canada, and collaborates closely with teams on both continents. You’ll work with experienced engineers who care about craft and move quickly.