AI/ML Engineer

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.