Job Title:Manager- Happy Robot
Job Location:Chennai
This roleisresponsible 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.
Key Responsibilities:
- 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:
- 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.
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- 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:
- 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.
RequiredQualification &Skills:
- Bachelor's orMaster'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.
- 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:
- 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.
- Comfort working in cross-functional teams (Product, BUIT, Compliance, Ops).
- Strong documentation and operational handover skills.
