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Unit Manager Technology as a Business
Job Location:
Monthly Salary:
Posted:
19 July 2026 (30+ days ago)
Posted:
Application Deadline:
16 October 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
| 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: 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 |
Required Experience:
Manager
About Company
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