Description :

How will you make an impact in this role?

We are looking for a seasoned Contact Center Architect with strong domain experience in conversational AI systems.

The Senior Engineer, Contact Center AI will design, build, deploy, and maintain AI/ML-powered capabilities for our contact center operations to improve automation, agent augmentation, self-service and customer satisfaction.

You will work cross-functionally with product, operations, data science, software engineering and QA to solve challenging problems around speech recognition, natural language understanding, dialog systems, routing, speech analytics and agent assist.

Roles & Responsibilities :

- Architect, design and maintain global conversational AI components for contact center applications including IVR systems, virtual agents, chatbots, and real-time assist ensuring scalability, resilience, and high-quality customer experiences.

- Contact Center Product knowledge like Google CCAAI, Amazon LEX, Genesys.

- Collaborate closely with business stakeholders, product owners and CX strategists to understand customer journeys, business objectives, and market trends, translating them into intuitive, human-centered conversational designs.

- Design and implement conversational flows and dialog management frameworks leveraging NLU/NLP models for intent classification, entity extraction, and context tracking, ensuring natural and context-aware interactions.

- Lead the development of speech-related modules such as speech-to-text, text-to-speech, voice biometrics, and speaker separation, optimizing for accuracy, latency, and noise robustness.

- Integrate AI-driven components with core contact center platforms (ACD, CTI, CRM, and telephony interfaces), ensuring seamless interoperability and end-to-end performance.

- Partner with data science and data engineering teams to build scalable data pipelines, manage model lifecycle (training, labeling, versioning), and ensure continuous improvement through analytics and feedback loops.

- Oversee the deployment, scaling, and maintenance of AI models and services using microservices, containerization, and orchestration frameworks (e.g., Kubernetes).

- Collaborate with business and analytics teams to define CX KPIs, SLAs, and success metrics, ensuring solutions deliver measurable business and customer impact.

- Mentor and guide engineers, conduct design and code reviews, and continuously drive architectural innovation and technical excellence.

- Stay current with emerging trends and research in conversational AI, speech technologies, and contact center analytics, bringing forward innovative ideas that enhance business outcomes and user experience.

Minimum Qualifications :

- Bachelors or masters degree in engineering or a related field

- 10+ years of experience in software development, with at least 23 years specifically in conversational AI, voice/telephony or contact center domain

- Strong programming skills in Python, Java, VXML or similar

- Hands-on experience in Google CES, Amazon Connect, Genesys or similar.

- Hands-on experience with NLP/NLU frameworks (e.g. spaCy, HuggingFace Transformers, Rasa, Dialogflow, LUIS, etc.)

- Experience with speech processing: ASR, TTS, acoustic modeling, voice signal processing

- Familiarity with MLOps tools, model serving, CI/CD pipelines

- Experience deploying AI/ML systems in cloud environments (AWS, GCP, Azure)

- Good understanding of REST APIs, microservices, containerization (Docker, Kubernetes)

- Working knowledge of data engineering, orchestration, feature stores

- Ability to optimize for performance, scalability, latency, memory constraints

- Strong problem-solving skills, ability to break down complex systems

- Excellent communication and collaboration skills, capable of working across teams

- Ability to lead, mentor and guide junior engineers

Preferred Qualifications :

- Experience in contact center technologies (ACD, IVR, telephony protocols, CTI interfaces)

- Experience with voice biometrics, sentiment analysis, voice analytics

- Experience with real-time streaming / voice pipelines

- Familiarity with reinforcement learning or dialog policy optimization

- Knowledge of call center metrics (e.g. average handle time, first contact resolution, CSAT)