AI Lead Engineer
Microsoft · Bengaluru
- Experience5–9 yrs
- SalaryNot disclosed
- Work modeonsite
- Posted24 Sept 2026
About Microsoft
Microsoft is hiring in Bengaluru in technology software. This role looks for around 5+ years of experience.
Skills
- ASR models
- large language models
- model fine-tuning
- training data curation
- synthetic data generation
- data augmentation
- deduplication
- data leakage detection
- model evaluation
- word error rate
- character error rate
- clinical coding
- knowledge distillation
- quantization
- streaming optimization
- latency optimization
- batching
- MLOps
- model serving
- model monitoring
- drift detection
- retrieval-augmented generation
- prompt engineering
The role
An AI engineer at a technology software company fine-tunes ASR models and large language models for clinical speech and reasoning, builds clinical evaluation systems, and optimizes model serving with quantization. This person leads applied AI direction and mentors engineers through rigorous experimentation.
Full job description
The core responsibilities for the job include the following:
Model Fine-Tuning (core of the role):
Fine-tune and adapt ASR models for domain vocabulary, accented and multilingual speech, noisy audio, and multi-speaker consultations.
Fine-tune LLMs for diagnostic and clinical reasoning, symptom extraction, differential generation, structured summarization, and clinical coding.
Run and debug real training jobs end-to-end, and diagnose runs that plateau, diverge, or catastrophically forget.
Decide when fine-tuning is the right answer versus prompting, retrieval, or a change of base model.
Data and Evaluation:
Own training data: curation, labeling strategy, synthetic generation, augmentation, deduplication, and leakage checks.
Build an evaluation that predicts production behavior: WER/CER sliced by accent and speaker, entity-level accuracy on clinical terms, hallucination, and omission rates.
Run clinician-in-the-loop review and convert qualitative complaints into measurable objectives.
Production and Optimization:
Distillation, quantization, streaming and latency optimization, batching, and cost per hour of audio.
Partner with platform/MLOps on serving, monitoring, drift detection, and retraining cadence.
Technical Leadership (approx. 20-25% of the role):
Set applied-AI direction for the team; run design reviews and make build-vs-buy calls.
Mentor 3-5 engineers on evaluation discipline and experiment hygiene.
Communicate model capability and limitations honestly to product, clinical, and leadership stakeholders.