Machine Learning Engineers

Role Name: ML Engineer At Kapture CX, we are looking for an ML Engineer in our AI/ML Team. Who are we? Kapture CX is a leading SaaS platform that helps enterprises automate and elevate customer experience through intelligent, AI-powered solutions. We partner with enterprises across industries to bring scalable automation and insight-driven efficiencies to their CX operations. Over a thousand clients across 18 countries have used Kapture’s products to enhance their customer experience, including Unilever, Reliance, Coca-Cola, Bigbasket, Meesho, Airtel Payments Bank and Cathay Pacific. Kapture delivers industry-specific solutions powered by AI, tailored workflows, and seamless automation. Kapture CX is headquartered in Bangalore and we have offices in Mumbai and Delhi/NCR in India, in addition to offices in the USA, UAE, Singapore, Philippines and Indonesia. What is this role all about? This is a model-first role where you will research, design, fine-tune, and evaluate AI models powering our conversational call agent platform. You will work across the full model development lifecycle — from understanding problems deeply and generating training data to implementing cutting-edge finetuning techniques and delivering production-ready models. Sounds interesting? Here’s a more detailed description of what you will do in this role: You will research, evaluate, and select foundation models (LLMs, ASR, TTS) based on performance and production constraints. You will develop end-to-end finetuning pipelines using techniques such as SFT, DPO, LoRA, QLoRA, and other efficient methods. You will design and implement robust evaluation frameworks including ASR metrics (WER/CER), TTS quality metrics (MOS/UTMOS), and LLM task-specific benchmarks. You will architect synthetic data generation pipelines to create scalable and high-quality training datasets. You will collaborate with MLOps teams during model handoff, ensuring clarity on model characteristics, inference requirements, latency, and quantization considerations. You will adapt models for Indian language and accent diversity, including Hindi, Hinglish, and regional variations. This is a Bangalore-based role. We work five days a week from the office, as we believe in-person interactions fuel innovation and agility. What does success look like in this role? You deliver well-characterised, production-ready models that improve conversational intelligence while balancing accuracy, efficiency, and real-world deployment constraints. What would make you a good fit for this role? Here are the basic requirements: You have 2–3 years of hands-on experience in ML model development, especially in LLMs, ASR, or TTS. You are proficient in PyTorch and understand training dynamics, debugging model instability, and optimization techniques. You have experience working with the HuggingFace ecosystem including Transformers, PEFT, TRL, and Datasets. You have practical experience with finetuning techniques such as SFT or DPO. What are the most critical skills for this role? You understand model architecture trade-offs including parameter size, attention mechanisms, KV cache behaviour, and quantization sensitivity. You can build maintainable, modular Python training pipelines. You design structured experiments and benchmarking frameworks for model evaluation. You communicate technical trade-offs clearly to engineering and infrastructure teams. You will have an advantage if you: You have experience finetuning Whisper-family, NeMo, ESPnet, or similar ASR models. You have worked with neural TTS systems and understand prosody modelling and code-mixed synthesis. You have experience finetuning open-source LLMs like LLaMA, Mistral, Qwen, or Gemma. You have exposure to synthetic data generation pipelines or multilingual NLP/speech systems. You use experiment tracking tools like Weights & Biases or MLflow. Why should you be interested? Here’s what you will gain from this role: Opportunity to work on cutting-edge AI powering real-world conversational systems. Hands-on exposure to LLM, ASR, and TTS model development at production scale. Collaboration with highly skilled engineering and AI teams in a fast-growing global SaaS organization. Strong growth opportunities with competitive compensation and performance-linked rewards.