• 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.

• As an AI Engineer, you will be the bridge between "what’s next" and "what works." • Lead technical execution in cloud-native environments (GCP/AWS) and build prototypes that solve real-world problems. • Own the technical delivery of innovation-focused initiatives from concept to implementation. • Collaborate with cross-functional teams to identify opportunities and shape solutions. • Rapidly prototype and iterate on new applications or internal tools to validate ideas. • Design, build, and deploy AI-enabled solutions using services like Vertex AI, LangChain, and LLM APIs. • Integrate machine learning models into internal tools, workflows, and user-facing products. • Develop prompt engineering workflows, orchestration patterns, and scalable AI solutions. • Architect cloud-native solutions in GCP and AWS, using best practices in scalability, security, and automation. • Implement infrastructure as code using tools such as Terraform or CloudFormation. • Support automation, API integration, and serverless deployments across multiple environments. • Contribute across a wide range of engineering activities, including scripting, automation, cloud setup, and backend development. • Solve ambiguous or undefined technical problems with practical and creative solutions. • Provide support for troubleshooting, debugging, and optimizing solutions post-deployment. • Evaluate emerging technologies and frameworks and recommend potential applications to support internal and external initiatives. • Document and share findings with internal teams to drive knowledge sharing and reusability.