• Play a critical role in advancing Cencora’s digital transformation agenda by building, deploying, and scaling AI and GenAI solutions • Work closely with AI Architects, AI Product Managers, and business stakeholders to translate initiatives into production-ready capabilities • Support the development and operation of Cencora’s internal GenAI platforms and AI-driven products; enabling data-driven decision making, intelligent automation, and digital workflows across the enterprise • Deliver new AI and GenAI capabilities that maximize business value, modernize core processes, and accelerate innovation in the global healthcare B2B market place • Emphasize cloud native engineering, responsible AI and scalable platform design

• Build and maintain data pipelines to support machine learning and analytics workflows. • Collect, clean, and transform large, complex datasets from engineering environments. • Develop and train predictive models for yield, performance, and anomaly detection. • Automate recurring data analysis tasks and integrate models into engineering processes. • Collaborate with design and software teams to embed AI-driven insights into products. • Create dashboards and visualization tools for reporting and decision-making. • Document code, models, and processes for transparency and reproducibility.

• Design, implement, and maintain production-grade AI services supporting NLQ and AI agent workflows. • Evaluate the NLQ system accuracy using quantitative and qualitative methods (precision/recall, semantic correctness, result equivalence). • Build automated evaluation pipelines and regression test suites integrated into CI/CD workflows. • Design and validate ontology-driven semantic models and knowledge graphs to improve NL answers accuracy and consistency. • Analyze NLQ failures and define structured error taxonomies, instrumentation, and logging to drive continuous improvement. • Develop and expose well-designed APIs for NLQ services, evaluation systems, and semantic layers. • Contribute to system architecture decisions including service boundaries, scalability, latency, and reliability tradeoffs. • Support cloud-native deployment, monitoring, and operational readiness of AI services.

• Design and implement real-time voice agents using ASR, TTS, and LLM-based reasoning. • Evaluate Voice Agent providers, focussing on reliability and stress testing • Build multi-turn, stateful voice conversations with interruption handling (barge-in), retries, tool-use and fallbacks. • Build evaluation for AI Agent performance, ensuring that AI Agents are working as expected • Finetune and adapt STT (Speech to Text), TTS (Text to speech) and LLMs for different dialects • Contribute to the development of the Lucidya AI Agent platform, ensuring that Voice capability is embedded in the platform • Deploy AI models into production environments with a focus on scalability, performance, and reliability. • Collaborate on MLOps practices to support continuous integration, monitoring, and improvement. • Work with streaming architectures (WebSockets, gRPC, SIP/WebRTC). • Analyse call logs and transcripts to continuously improve performance. • Work closely with software engineers, product managers, and domain experts • Translate business problems into effective AI solutions with measurable outcomes. • Ensure AI solutions comply with ethical standards, data privacy requirements, and legal regulations. • Document models, algorithms, workflows, and decisions clearly for internal and external stakeholders. • Stay up to date with the latest AI research, tools, and best practices; and apply them where valuable.