AI Engineer

We are seeking a highly skilled hybrid GenAI and machine learning engineer to design, test, and deploy Generative AI (GenAI), Large Language Model (LLM)-powered capabilities and machine learning solutions within the contact center. This role focuses on hands‑on development, LLM evaluation, prompt engineering, and solution testing, working closely with product managers and operations teams. You will help build AI-driven solutions across the contact lifecycle and drive meaningful performance and customer experience improvements. 

Position Responsibilities    

Key Responsibilities – Solution Development 

• Develop, train, and deploy machine learning models to support contact center optimization and quality automation. 

• Build and maintain ML pipelines for data prep, feature engineering, model training, testing, and monitoring. 

• Apply MLOps best practices including CI/CD, model versioning, drift detection, and performance monitoring. 

• Develop, configure, and test GenAI solutions for contact center workflows including agent assist, summarization, knowledge retrieval, and QA automation. 

• Build prompt frameworks and templates for operational use cases. 

• Evaluate and compare LLM capabilities across platforms (OpenAI, Azure OpenAI, Anthropic, Google, etc.). 

• Conduct iterative experimentation to improve model accuracy, tone, compliance, and consistency. 

• Integrate LLMs with enterprise data sources using retrieval-augmented generation (RAG). 

Key Responsibilities – LLM Evaluation & Quality Assurance 

• Design evaluation methodologies including grounding tests, hallucination detection, correctness scoring, and safety checks. 

• Create scoring rubrics and benchmark datasets tailored to contact center scenarios. 

• Run side-by-side model comparisons and maintain dashboards for accuracy, latency, and cost. 

• Test solutions against compliance, risk, and privacy guidelines. 

Key Responsibilities – Technical Solutioning & Prototyping 

• Build prototypes using Python, APIs, vector databases, embeddings, and orchestration frameworks. 

• Experience developing and deploying ML models (supervised, unsupervised, deep learning) in production. 

• Skilled with ML frameworks such as TensorFlow, PyTorch, scikit-learn. 

• Familiarity with MLOps tools (MLflow, Kubeflow, Azure ML). 

• Strong understanding of model lifecycle management and monitoring. 

• Develop pipelines to prepare transcripts, metadata, and knowledge sources for GenAI. 

• Use tools such as Azure OpenAI, LangChain, or Semantic Kernel. 

• Partner with engineering to hand off production-ready logic and evaluation artifacts. 

Required Qualifications: 

• Hands-on experience with custom ML model development 

• Strong Python skills (pandas, numpy, scikit-learn, LangChain/Semantic Kernel) 

• Understanding of NLP concepts including summarization, classification, and semantic similarity 

• Experience with large conversational datasets (voice, chat, digital) 

• Familiarity with cloud services (e.g., AWS Bedrock, Connect) 

Preferred Qualifications: 

• Hands-on experience with LLMs, prompt design, and evaluation 

• Experience implementing RAG systems with vector databases such as Pinecone, Milvus, or ChromaDB. 

• Experience working with APIs, embeddings, vector databases, and knowledge bases 

• Knowledge of responsible AI, safety evaluation, and compliance testing. 

When you join our team: 

• We’ll empower you to learn and grow the career you want. 

• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words. 

• As part of our global team, we’ll support you in shaping the future you want to see. 

#LI-Hybrid