About the role
structured by ORIExperience: 1-2 years of experience. Role & Responsibilities Design, develop, and deploy end-to-end Machine Translation (NMT), Speech-to-Text (STT), and Text-to-Speech (TTS) systems.
What you will do
- Design, develop, and deploy end-to-end Machine Translation (NMT), Speech-to-Text (STT), and Text-to-Speech (TTS) systems.
- Build and adapt transformer-based and sequence-to-sequence architectures for multilingual and low-resource learning scenarios.
- Build robust data pipelines for ingestion, cleaning, data augmentation, synthetic data generation, and back-translation.
- Train and fine-tune models using PyTorch, Hugging Face Transformers, Fairseq, or OpenNMT with PEFT/LoRA and mixed-precision techniques.
- Optimize model latency, memory footprint, and throughput using ONNX, TensorRT, and TorchScript for offline/on-device edge deployment.
What they are looking for
- 1.5 to 2+ years of hands-on experience in Machine Learning / AI engineering, specifically within NLP, Speech Processing, or Machine Translation.
- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
- Strong Python programming proficiency with deep experience in PyTorch, Hugging Face Transformers, Seq2Seq models, and ONNX/TensorRT optimization.
- Hands-on experience building REST APIs (FastAPI/Flask), Docker containerization, and deploying models in edge, on-premise, or offline environments.
- Strong problem-solving mindset with the ability to bridge research prototypes into production-grade systems.
Nice to have
- Familiarity with multilingual LLMs, distributed training (DeepSpeed/FSDP), vector search (FAISS/Milvus), and MLOps pipelines (Airflow, GitHub Actions).
Full posting text
Experience: 1-2 years of experience.
Role & Responsibilities
Design, develop, and deploy end-to-end Machine Translation (NMT), Speech-to-Text (STT), and Text-to-Speech (TTS) systems.
Build and adapt transformer-based and sequence-to-sequence architectures for multilingual and low-resource learning scenarios.
Build robust data pipelines for ingestion, cleaning, data augmentation, synthetic data generation, and back-translation.
Train and fine-tune models using PyTorch, Hugging Face Transformers, Fairseq, or OpenNMT with PEFT/LoRA and mixed-precision techniques.
Optimize model latency, memory footprint, and throughput using ONNX, TensorRT, and TorchScript for offline/on-device edge deployment.
Build scalable inference APIs and microservices using FastAPI, Flask, or gRPC containerized with Docker.
Set up model benchmarking, error analysis, and evaluation pipelines using metrics like BLEU, WER, ROUGE, and METEOR.
Track experiments, model drift, and performance using MLflow, Weights & Biases, or TensorBoard.
Preferred Candidate Profile
Experience: 1.5 to 2+ years of hands-on experience in Machine Learning / AI engineering, specifically within NLP, Speech Processing, or Machine Translation.
Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
Core Technical Skills: Strong Python programming proficiency with deep experience in PyTorch, Hugging Face Transformers, Seq2Seq models, and ONNX/TensorRT optimization.
Deployment & Systems: Hands-on experience building REST APIs (FastAPI/Flask), Docker containerization, and deploying models in edge, on-premise, or offline environments.
Bonus Qualifications: Familiarity with multilingual LLMs, distributed training (DeepSpeed/FSDP), vector search (FAISS/Milvus), and MLOps pipelines (Airflow, GitHub Actions).
Mindset: Strong problem-solving mindset with the ability to bridge research prototypes into production-grade systems.
Key skills: Speech Recognition, Machine Translation, Natural Language Processing (NLP), PyTorch, DeepSpeed, Hugging Face, ONNX, TensorRT, Deep Learning, Sequence-to-Sequence, Docker, LoRA, Transformers, FastAPI, Python.
Role: Machine Learning Engineer
Industry Type: IT Services & Consulting
Department: Data Science & Analytics
Employment Type: Full Time, Permanent
Role Category: Data Science & Machine Learning
Education: UG: B.Tech / B.E. in Computer Science and Engineering (CSE), Artificial Intelligence And Machine Learning
Education: PG: M.Tech in Computers