Gen AI Engineer - Audio / Video

AVE Promagne; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

AVE Promagne is seeking a skilled AI/ML engineer to transform live consultations into structured, clinically accurate documents with sub-second latency.

You will fine-tune models (LoRA/PEFT/SFT), develop robust speech-to-text and NER pipelines, and build end-to-end evaluation and deployment workflows in a healthcare-grade environment. Collaboration with clinicians to ensure reliability is essential.

Qualifications

• Bachelor's in Computer Science, Engineering, or a related field.
• 3+ years in AI/ML, with a focus on applied and Generative AI systems.
• Strong hands-on experience with LLMs, prompt engineering, and structured outputs.
• Hands-on with speech-to-text and audio-processing pipelines.
• Proficient in Python (pandas, NumPy, ML/AI libraries) and SQL.
• Familiar with cloud AI services, preferably AWS (S3, SageMaker, Bedrock).
• Understanding of LLMOps / MLOps: evaluation, monitoring, deployment, and iteration.

Responsibilities

• Real-time clinical document generation - turn a live consultation into a structured, clinically accurate document while the clinician is still in the room.
• Fine-tuning special-purpose models -LoRA, PEFT, SFT and preference alignment, optimized for highthroughput serving.
• Speech-to-text and audio pipelines - real clinical audio: multiple speakers, accents, noise, and Swedish and German medical vocabulary.
• Medical NER and structured extraction - medications, dosages, diagnoses, procedures and temporal relationships pulled out of free text, reliable enough to write into a patient record.
• Evaluation pipelines - automated judges, clinician-in-the-loop review, and monitoring that catches regressions before a clinic does.
• Automated booking and scheduling - agentic systems that handle real patient interactions end to end.
• Data and deployment - curate clinical datasets to a training-ready standard, and production-grade services clinicians rely on at scale.

Skills

LLMs

Prompt engineering

Structured outputs

Speech-to-text

Python

SQL

AWS

LLMOps

Education

Bachelor's in Computer Science, Engineering, or a related field

Tools

SageMaker

Bedrock

Job description

What you'll do

• Real-time clinical document generation - turn a live consultation into a structured, clinically accurate document while the clinician is still in the room. Sub-second latency, zero tolerance for hallucination.
• Fine-tuning special-purpose models -LoRA, PEFT, SFT and preference alignment, optimized for highthroughput serving. Dataset design through to production. This is where we invest in going deep.
• Speech-to-text and audio pipelines -real clinical audio: multiple speakers, accents, noise, and Swedish and German medical vocabulary no off-the-shelf model has seen.
• Medical NER and structured extraction -medications, dosages, diagnoses, procedures and temporal relationships pulled out of free text, reliable enough to write into a patient record.
• Evaluation pipelines -in healthcare, "it looks good" is not a metric. Golden datasets, automated judges, clinician-in-the-loop review, and the monitoring that catches regressions before a clinic does.
• Automated booking and scheduling -agentic systems that handle real patient interactions end to end, with real-world consequences when they go wrong.
• Data and deployment -curate clinical datasets to a training-ready standard, and take systems from experiment to monitored, production-grade services clinicians rely on at scale. You won't do all of this at once, but over time you'll touch most of it.

What we're looking for

• Bachelor's in Computer Science, Engineering, or a related field.
• 3+ years in AI/ML, with a focus on applied and Generative AI systems.
• Strong hands-on experience with LLMs, prompt engineering, and structured outputs.
• Hands-on with speech-to-text and audio-processing pipelines.
• Proficient in Python (pandas, NumPy, ML/AI libraries) and SQL.
• Familiar with cloud AI services, preferably AWS (S3, SageMaker, Bedrock).
• Understanding of LLMOps / MLOps: evaluation, monitoring, deployment, and iteration.