- *Job Title:** Manager – Happy Robots \- GenAI automation support
This role is responsible for configuring, deploying, operating, and continuously improving AI\-powered automation solutions on the HappyRobots platform. This role focuses on low\-code / no\-code GenAI product configuration and ensures AI solutions are production\-ready, compliant, scalable, cost\-optimized, and ethically sound. The position serves as a critical execution layer between Business Unit IT (BUIT) priorities and real\-world AI deployments, enabling reliable and responsible AI automation across digital and voice channels.
- **GenAI Product Configuration \& Deployment**
- Configure and deploy low\-code/no\-code GenAI\-powered conversational and automation solutions on the HappyRobots platform.
- Implement workflows, business rules, orchestration logic, and integrations based on BUIT\-defined priorities and solution designs.
- Perform prompt engineering, policy engineering, and guardrail configuration for LLM\-powered agents.
- Configure multi\-channel AI experiences across voice, email, SMS, and chat.
- **Data Preparation, Annotation \& Model Enablement**
- Perform data annotation, labeling, cleansing, and validation for structured and unstructured datasets.
- Design and generate synthetic data when real data is insufficient or unavailable.
- Support training, fine\-tuning, testing, and evaluation of AI models (including LLM\-based workflows).
- Ensure data quality, lineage, and traceability across training and inference pipelines.
- **Testing, Validation \& Responsible AI**
- Conduct functional, performance, and regression testing of configured AI solutions.
- Prepare audit logs, model cards, decision records, and test documentation.
- Evaluate AI solutions for:
- + Bias and fairness
+ Ethical compliance
+ Explainability and transparency
- Ensure adherence to Responsible AI, data privacy, and regulatory standards.
- **Production Support \& Continuous Improvement**
* Monitor AI solutions in production for
- + Accuracy and response quality
+ Latency, availability, and throughput
+ Cost and token usage optimization
- Perform issue analysis, root cause identification, and corrective actions.
- Implement continuous improvements through prompt refinement, workflow optimization, and configuration updates.
- **MLOps \& Platform Operations**
- Support model lifecycle management, including versioning, upgrades, rollback strategies, and registry management.
- Assist with platform and model upgrades while ensuring solution stability.
- Collaborate on deployment pipelines, monitoring dashboards, and alerting mechanisms.
- Support scaling, reliability, and resilience of AI solutions.
- **Integration \& Backend Enablement**
- Configure and manage API integrations with internal systems and third\-party platforms.
- Support data flows across conversational agents, databases, and enterprise systems.
- Work with backend services for authentication, security, and system interoperability.
- **Operational \& Automation Use\-Case Enablement**
* Enable AI Workers and automation use cases such as
+ Vendor coordination
+ Shipment tracking
+ Document ingestion and data entry
- Configure contextual understanding in TTS and voice\-based AI, including tone, rhythm, and intent fidelity.
- Support document processing workflows, including extraction, validation, and system handoffs.
- *Required Qualification \& Skills:**
- Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field.
- Minimum 3 years of relevant experience in the GenAI domain
- Proficiency in Python (mandatory) for AI workflows, automation, and data processing.
- Full\-stack experience with React, TypeScript, and Node.js.
- Strong understanding of APIs, backend services, and system integrations.
- Hands\-on experience building and operating AI\-powered applications.
- Practical expertise in:
- + Large Language Model (LLM) prompting and tuning
+ Prompt orchestration and policy engineering
+ Understanding of ML/DL fundamentals
- Experience working with conversational AI, NLP, and GenAI platforms.
- Experience with data pipelines, preprocessing, and dataset management.
- Exposure to MLOps practices, including:
- + Model deployment
+ Monitoring and evaluation
+ Scaling and cost optimization
- Familiarity with model/version registries and lifecycle management.
- Working knowledge of database design and processing (SQL/NoSQL).
- Understanding of data modeling for conversational and automation workloads.
- Advanced analytical and reasoning abilities to interpret AI behavior and outcomes.
- Experience configuring multi\-channel conversational systems (voice and digital).
- Strong grasp of workflow coordination and automation logic.
- Understanding of context\-aware TTS systems and voice AI design considerations.
- Hands\-on experience with document processing and intelligent data entry workflows.
- Experience with low\-code / no\-code AI platforms or enterprise automation tools.
- Familiarity with Responsible AI frameworks, model governance, and compliance controls.
- Exposure to cloud environments (Azure, AWS, or GCP) in AI deployments.
- Understanding of cost controls and token management for LLM\-based systems.