Machine Learning Engineer

About Aiverbalyze

Aiverbalyze Technologies is a deep-tech communication intelligence company building domain-adapted AI models that humanize machine conversations. Our proprietary stack includes optimized LLMs, multilingual ASR/TTS, Visual Language Models, and Neural Machine Translation all trained on region-specific, domain-rich datasets covering 30+ Indian and global languages.

We power intelligent voice, chat, and email automation for enterprises across BPO, healthcare, banking, and telecom running on both edge and cloud with quantized deployments for high-throughput, ultra-low-latency inference.

Role overview

We are looking for a passionate and technically sharp ML Engineer Intern to join our core AI team as paid internship with full time job prospects. You will work directly on training, fine-tuning, and optimizing large language models, speech models, and multimodal architectures and deploying them at production scale. This is a hands-on, deep-tech role with real ownership from day one.

Outstanding interns will receive a full-time job offer upon completion.

What you will work on

• Fine-tuning and domain-adapting LLMs (Qwen, LLaMA, Mistral variants) for enterprise conversation use cases
• Training and optimizing multilingual ASR/TTS models using NeMo, ESPnet, or similar frameworks
• Running distributed training jobs (DDP/FSDP) on multi-GPU clusters and debugging training instabilities
• Implementing quantization (INT4/INT8 via AWQ, GPTQ, BitsAndBytes) and running benchmarks for accuracy-latency tradeoffs
• Optimizing inference pipelines using vLLM, TensorRT-LLM, or ONNX Runtime for low-latency deployment
• Building and curating domain-specific datasets cleaning, deduplication, augmentation, and formatting for instruction tuning
• Evaluating models on real-world benchmarks: WER, BLEU, ROUGE, and custom domain evals
• Collaborating with the product and platform team to deploy models via Dockerized services and REST APIs

What we are looking for

• Final year B.Tech / M.Tech / MS student in CS, EE, Data Science, or a related field or a recent graduate
• Strong Python skills and comfort with PyTorch; experience with HuggingFace Transformers is a must
• Understanding of transformer architectures (attention, positional encoding, tokenization)
• Familiarity with training concepts: loss functions, optimizers, learning rate schedules, gradient checkpointing
• Exposure to at least one of: NeMo, vLLM, TGI, TensorRT, ONNX even from personal projects
• Comfortable working in Linux/Docker environments and reading model/system logs
• Curiosity, ownership mindset, and ability to read research papers and translate them to code

Good to have

• Experience with speech models Whisper, NeMo FastConformer, Wav2Vec, or similar
• Knowledge of RLHF, DPO, or instruction tuning pipelines
• Familiarity with multilingual NLP Indian language datasets, transliteration, code-switching
• Prior internship, research project, or Kaggle experience in NLP or speech
• Contributions to open-source ML repos