About the Role:

We are looking for a hands-on AI/ML Engineer to design, build, and deploy multilingual NLP and speech systems, including Neural Machine Translation (NMT), Speech-to-Text (STT), and Text-to-Speech (TTS). This role combines applied research and engineering with a strong focus on multilingual modeling, low-resource learning, and offline/on-device (edge) deployment.

Key Responsibilities:

Multilingual Modeling & NLP Systems:

- Design and develop Machine Translation (NMT), Speech-to-Text (STT), and Text-to-Speech (TTS) systems.

- Work with transformer-based and sequence-to-sequence architectures.

- Adapt models for multilingual and low-resource scenarios.

Data Engineering & Processing:

- Build pipelines for data ingestion, cleaning, and preprocessing.

- Handle low-resource and noisy datasets.

- Implement data augmentation, back-translation, and synthetic data generation.

- Work with JSON, Parquet, TFRecords.

Model Training & Optimization:

- Train models using PyTorch, TensorFlow, or JAX.

- Use Hugging Face Transformers, Fairseq, OpenNMT.

- Apply transfer learning and PEFT (LoRA, Adapters).

- Optimize with mixed precision and distributed training.

- Improve latency, throughput, and memory efficiency.

Evaluation & Experimentation:

- Design evaluation pipelines (BLEU, WER, ROUGE, METEOR).

- Use MLflow, Weights & Biases, TensorBoard.

- Perform benchmarking, error analysis, and regression testing.

Deployment & Edge Systems:

- Deploy models in offline/on-device environments.

- Build inference APIs using FastAPI, Flask, or gRPC.

- Work with Docker and Kubernetes.

- Optimize inference using ONNX, TensorRT, TorchScript.

AI Systems & MLOps:

- Develop pipelines using Airflow or Prefect.

- Implement CI/CD (GitHub Actions, Jenkins).

- Monitor model performance and drift.

Research & Innovation:

- Explore multilingual LLMs and speech-language models.

- Prototype and evaluate new approaches.

- Contribute to internal frameworks and tools.

Collaboration:

- Work with ML engineers, data scientists, and research teams.

- Collaborate with linguistic and annotation teams.

- Document systems and experiments.

Tech Stack & Good to Have:

- Advanced Concepts: Multilingual LLMs, Transfer learning, domain adaptation, Real-time inference systems.

- Additional Tools: FAISS, Milvus, Weaviate, Airflow, Prefect, GitHub Actions, Jenkins, DeepSpeed, FSDP.

What We're Looking For:

- Strong problem-solving skills.

- Ability to work in research-driven environments.

- Hands-on builder mindset.

- Good communication skills.

Why Join Us:

- Work on cutting-edge multilingual AI and speech systems.

- Build production-grade AI solutions.

- Exposure to LLMs, speech AI, and edge deployment.