Unit Manager / Senior Unit Manager - Technology as a Business

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) 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: 36 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