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Overview We are looking for a skilled AI/ML Engineer to develop, optimize, and maintain intelligent speech-to-text and medical NLP systems. The ideal candidate will work on automating transcription workflows, improving accuracy, reducing turnaround time, and enhancing the quality of medical documents. Key Responsibilities Develop and fine-tune speech recognition models for doctor dictations (UK, US, India accents). Build NLP pipelines for medical terminology, abbreviations, and context understanding. Implement error detection, auto-correction, and QA automation in transcription outputs. Integrate with voice services such as AWS Transcribe Medical, Azure Cognitive Speech, or Google Speech-to-Text. Create APIs for seamless integration with existing transcription tools, EMR/EHR systems, and workflow software. Work closely with QA and transcription teams to continuously improve model accuracy using real transcription datasets. Ensure data security and HIPAA/GDPR compliance in model training and storage. Required Technical Skills Strong knowledge of Python Deep understanding of NLP & Machine Learning Experience in speech recognition frameworks such as: Whisper / Vosk / DeepSpeech / Kaldi Familiarity with transformer models: BERT, BioBERT, ClinicalBERT, GPT-based medical NLP Experience with Git for code management Experience with cloud services (any one of the following): AWS, Azure, or GCP Preferred Skills (Added Advantage) Medical terminology understanding (ICD-10, drugs, symptoms, CPT codes) Experience with FastAPI or Django for backend services Knowledge of data labeling, speech dataset creation, and model evaluation Exposure to LLM fine-tuning or prompt engineering Soft Skills Clear communication and analytical thinking Problem-solving with minimal supervision Willingness to experiment and build proof-of-concept models Ability to work closely with cross-functional teams (QA, medical reviewers, transcription operators) Experience Required 1–3 years in AI/ML, NLP, or Speech Recognition (Freshers with strong projects are welcome if they show capability in NLP/speech tasks)
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.