Senior Software Engineer, AI

Verint•Remote, Canada

About The Position

At Verint, we believe customer engagement is the core of every global brand. Our mission is to help organizations elevate Customer Experience (CX) and increase workforce productivity by delivering CX Automation. We hire innovators with the passion, creativity, and drive to answer constantly shifting market challenges and deliver impactful results for our customers. Our commitment to attracting and retaining a talented, diverse, and engaged team creates a collaborative environment that openly celebrates all cultures and affords personal and professional growth opportunities. Learn more at www.verint.com. Are you driven by innovation and looking to thrive in a fast-paced, growing environment? Join us at Calabrio-Verint and be part of our dynamic team! Help us in reshaping the landscape of customer experience – where every interaction becomes an opportunity, and every insight drives meaningful change. Calabrio-Verint are the trailblazers in customer experience intelligence! Revolutionizing the way organizations connect with their customers, we empower businesses to elevate every interaction to new heights. Our cutting-edge cloud platform, coupled with AI-driven analytics tools, unlocks the true essence of customer sentiment, turning data into actionable insights with lightning speed. Calabrio-Verint is looking for a highly skilled and experienced Software Engineer, AI to perform a key role in our digital transformation program, and deliver exceptional customer experience supported by trusted, and resilient business solutions. As an AI Software Engineer, you will design, build, deploy, and optimize AI-powered products and platforms, with a strong focus on LLM applications, agentic AI systems, applied machine learning, backend engineering, data pipelines, evaluation, and production operations. You will turn AI capabilities into reliable business solutions that are scalable, measurable, secure, and maintainable. This role is ideal for someone who can move beyond experimentation and deliver production-grade AI systems, including autonomous and semi-autonomous AI agents that can reason, plan, use tools, retrieve knowledge, and take actions safely within defined business workflows. Calabrio has embarked a journey and is truly committed to establishing a value fabric that transforms its customer, employee, and stakeholder experiences through seamless integrated, agile, data-driven, and secure Digital Services. Such an endeavor requires leaders passionate about customer experience and committed to consistently delivering value while focusing on digital services with inherent trust and resilience.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field required. Master’s degree preferred.
5+ years of end-to-end experience training, evaluating, testing, deploying, and monitoring machine learning models in production.
Hands-on experience building applications with LLMs, prompt engineering, retrieval-augmented generation, structured outputs, and model evaluation.
Experience with frameworks or platforms for LLM and agent orchestration, such as LangChain, LangGraph, Strands AI, or equivalent architectures.
Experience designing or building AI agents that use planning, memory, tool calling, workflow orchestration, agent-to-agent and external system integration to complete multi-step tasks.
Strong experience with Python and backend frameworks such as Flask or Django for building production APIs and AI services.
Strong understanding of machine learning fundamentals and practical experience with NLP tasks such as text classification, NER, clustering, topic modeling, semantic search, and conversational AI.
Experience with fine-tuning LLMs and transformer-based models such as BERT, RoBERTa, ALBERT, and a solid understanding of tokenizers, embeddings, pre-trained models, and adaptation techniques.
Experience with SQL and NoSQL databases, vector databases or embedding stores, and data pipelines for AI applications.
Experience with model serving, observability, evaluation, error analysis, prompt/version management, and monitoring of AI systems in production.
Familiarity with Linux systems and standard software engineering practices including testing, CI/CD, APIs, and version control.

Nice To Haves

Experience with AWS, Azure, or GCP
Experience with Docker and Kubernetes
Experience with ETL and Data Engineering projects
Experience with PostgreSQL, Snowflake, or MongoDB
Experience with Kubeflow, or Airflow

Responsibilities

Design AI systems
Build end-to-end AI solutions using machine learning, deep learning, NLP, and generative AI technologies.
Develop LLM-powered applications
Create applications using foundation models, prompt engineering, retrieval-augmented generation, structured outputs, function/tool calling, and agent workflows.
Build agentic AI solutions
Design and implement AI agents that can plan, reason through multi-step tasks, interact with external tools and APIs, retrieve relevant context, and execute actions within controlled business processes.
Develop multi-agent and orchestration workflows
Create orchestrated AI systems where multiple agents or components collaborate to solve complex tasks, with clear control flow, observability, and fallback handling.
Productionize models and AI agents
Deploy, monitor, and maintain AI/ML models and agentic systems in production environments with strong reliability, performance, and safety standards.
Build data and inference pipelines
Develop pipelines for ingestion, preprocessing, vector search, model inference, agent memory/context management.
Improve quality and evaluation
Define offline and online evaluation frameworks for model quality, latency, safety, task completion, agent reliability, and business outcomes.
Optimize performance and cost
Improve model selection, prompt efficiency, agent orchestration, latency, throughput, caching, token usage, and serving efficiency.
Ensure governance and safety
Apply best practices for security, privacy, responsible AI, model risk controls, guardrails, agent permissions, compliance, and human-in-the-loop review where needed.