About the role

Senior AI Software Engineer building production LLM applications, agents, and machine-learning platforms. Calabrio-Verint uses customer experience intelligence and AI analytics to improve business interactions.

Responsibilities

Design AI systems
Build end-to-end AI solutions using machine learning, deep learning, NLP, and generative AI
Develop LLM-powered applications using foundation models, prompt engineering, retrieval-augmented generation, structured outputs, function/tool calling, and agent workflows
Design and implement AI agents that plan, reason through multi-step tasks, interact with tools and APIs, retrieve context, and execute controlled business-process actions
Develop multi-agent and orchestration workflows with control flow, observability, and fallback handling
Deploy, monitor, and maintain AI/ML models and agentic systems in production
Build ingestion, preprocessing, vector-search, model-inference, and agent memory/context pipelines
Define offline and online evaluation frameworks for quality, latency, safety, task completion, reliability, and business outcomes
Optimize model selection, prompt efficiency, orchestration, latency, throughput, caching, token usage, and serving efficiency
Apply security, privacy, responsible AI, model-risk controls, guardrails, permissions, compliance, and human-in-the-loop review
Devise and implement advanced NLP algorithms and LLM models for Conversation Intelligence analytics
Collaborate with internal and external stakeholders and incorporate input from various sources

Bachelor’s degree in Computer Science, Engineering, or a related field required
5+ years of end-to-end experience training, evaluating, testing, deploying, and monitoring machine learning models in production
Hands-on experience with LLMs, prompt engineering, retrieval-augmented generation, structured outputs, and model evaluation
Experience with LangChain, LangGraph, Strands AI, or equivalent LLM/agent orchestration architectures
Experience designing or building AI agents using planning, memory, tool calling, workflow orchestration, agent-to-agent, and external system integration
Strong Python experience and backend frameworks such as Flask or Django
Strong understanding of machine learning fundamentals
Practical NLP experience including text classification, NER, clustering, topic modeling, semantic search, and conversational AI
Experience fine-tuning LLMs and transformer-based models such as BERT, RoBERTa, and ALBERT
Understanding of tokenizers, embeddings, pre-trained models, and adaptation techniques
Experience with SQL and NoSQL databases, vector databases or embedding stores, and AI data pipelines
Experience with model serving, observability, evaluation, error analysis, prompt/version management, and production monitoring
Familiarity with Linux, testing, CI/CD, APIs, and version control
Excellent communication, collaboration, critical thinking, analytical, and problem-solving skills