Manager - Business Support- Happy Robot

Job Title: Manager – Happy Robot

Job Location: Chennai

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.

Key Responsibilities:

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. Operational & Automation Use-Case Enablement

• Enable AI Workers and automation use cases such as:

• Appointment scheduling

• 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.

• Comfort working in cross-functional teams (Product, BUIT, Compliance, Ops).

• Strong documentation and operational handover skills.