Research Engineer - NLP/Machine Learning

Job Description :

Key Responsibilities :

- Design and implement ML/NLP-driven solutions, focusing on LLMs, ASR/STT, and modern AI frameworks.

- Develop, optimize, and maintain production-grade ML workflows and pipelines.

- Work with APIs, system design, and integrations for scalable ML solutions.

- Experiment with LLM prompt engineering, chaining, fine-tuning, and RAG pipelines.

- Collaborate with data scientists and engineers to build and deploy ML models.

- Leverage tools such as Transformers, Hugging Face, LangChain, and OpenAI APIs for model development.

- Deploy and monitor ML systems on cloud platforms (AWS/GCP).

- Maintain code quality and version control using Git, Docker, and best practices.

- Troubleshoot, profile, and optimize performance/latency of ML inference systems.

Requirements :

- 5- 6 years of hands-on software engineering experience, ideally in ML/NLP-heavy roles.

- Strong proficiency in Python with expertise in libraries like NumPy, Pandas, etc.

- Practical experience with LLMs (GPT, Llama, Mistral), prompt engineering, chaining, or fine-tuning.

- Exposure to ASR/STT technologies (Whisper, DeepSpeech, Kaldi).

- Strong understanding of system design, API integrations, and ML workflows in production.

- Experience with Transformers, Hugging Face, LangChain, OpenAI APIs.

- Knowledge of AWS/GCP and model deployment basics.

- Familiarity with Git, Docker, and collaborative version control workflows.

- Bonus : Background in competitive programming or strong DSA skills.

Nice to Have :

- Experience with speech/audio data pipelines or ASR model fine-tuning.

- Knowledge of RAG pipelines, embeddings, and vector databases.

- Skills in performance profiling, latency debugging, and scaling inference systems.