Unit Manager - Technology as a Business

Job description

**Job Purpose** The Senior AI Engineer role exists to design, build, and operationalize production-grade AI models and pipelines, enabling scalable Voice AI and Generative AI solutions aligned with business use cases. The role focuses on hands-on development, optimization, and deployment of AI systems, translating architectural vision into robust, high-performing solutions. **Duties and Responsibilities** - Build and deploy Speech AI and LLM-based systems (STT, TTS, S2S, dialogue orchestration) - Implement production-grade pipelines for inference, fine-tuning, and model lifecycle - Work on low-latency, high-throughput model serving (real-time voice systems) - Optimize models using quantization, distillation, pruning techniques - Integrate LLMs/SLMs into voice workflows (prompting, chaining, orchestration) - Develop emotion-aware dialogue handling logic and fallback strategies - Support voice biometrics and anti-spoofing system implementation - Work closely with Product and Lead AI to translate business problems into AI solutions - Ensure model performance, monitoring, observability, and continuous improvement - Build and convert POCs into stable production deployments (no demo-only work) - Follow best practices in MLOps, versioning, and reproducibility **Key Decisions / Dimensions** - Model implementation choices (fine-tune vs prompt vs orchestration) - Selection of frameworks, libraries, and deployment patterns - Trade-offs between performance vs cost vs scalability - Decisions on model optimization techniques (quantization, distillation, etc.) - Integration approach for LLMs with speech pipelines - Handling edge cases in dialogue flow and failure scenarios **Major Challenges** - Making models production-ready (latency, stability, cost) not just proof of concept - Handling noisy real-world voice inputs across languages and dialects - Balancing accuracy vs latency vs infra cost constraints - Integrating multiple AI components (STT + LLM + TTS) without breaking flow - Managing model degradation and continuous learning loops from failures - Working within real-world infra limitations (GPU availability, edge constraints) **Required Qualifications and Experience** - Bachelors or Masters degree in Computer Science, AI, or related field - Experience: 3-6 years in AI/ML with strong hands-on delivery - Strong experience in Speech AI (STT, TTS, S2S) - Hands-on experience with LLMs/SLMs (OpenAI, HuggingFace, LangChain) - Experience in real-time AI systems / low-latency inference pipelines - Proficiency in Python, PyTorch / TensorFlow - Experience with model optimization (quantization, distillation) - Knowledge of MLOps, deployment pipelines, and model monitoring - Understanding of dialogue systems and conversational AI flows - Exposure to voice biometrics / anti-spoofing (good to have) **Nice to Have** - Experience with Indic languages / dialect-heavy environments - Hands-on work in production AI (not just research/POC) - Exposure to edge AI / on-device deployment