We are looking for a skilled and hands-on Machine Learning Engineer with a strong foundation in Python, ML engineering tools, and applied experience in building and managing production-grade ML pipelines. Youll work on end-to-end ML systems, from data processing and feature engineering to model deployment and monitoring.

This role requires a deep understanding of ML lifecycle, experience with LLMs and RAG pipelines, and the ability to optimize distributed data and compute systems.

Key Requirements :

Core Skills :

- Proficient in Python

- Experience with at least one cloud platform (GCP preferred but not mandatory)

- Strong understanding of the end-to-end ML lifecycle

- Experience with ML modeling, evaluation metrics, and model monitoring

- Familiar with ML frameworks such as TensorFlow or PyTorch

ML Engineering Tools & Infrastructure :

- Experience with Docker and managing environments (venv, pip, poetry, etc.)

- Exposure to orchestrators like Vertex AI Pipelines, Airflow, etc.

- Advanced SQL skills

- Familiarity with CI/CD pipelines, deployment methodologies, and infrastructure automation (e.g., Terraform)

- Hands-on with distributed computing and tools like Apache Spark, Beam, or Flink

Data Engineering :

- Strong grasp of data and feature engineering techniques

- Understanding of streaming concepts such as windowing, late arrival, and triggers

- Experience with designing data architectures and pipeline optimization

LLM & RAG :

- Exposure to LLM-based workflows : embeddings generation, indexing, Retrieval-Augmented Generation (RAG), agents, etc.

- Familiarity with vector databases like Qdrant

Education :

- B.Tech / B.E. in Computer Science, Data Science, Information Technology, or a related technical field

- Equivalent practical experience may also be considered