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
• Bachelor’s or Master’s 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 (valuable 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

📌 Unit Manager - Technology as a Business/Senior Unit Manager - Technology as a Business (Pune)
🏢 Bajaj Finance
📍 Pune