Role Overview:

We are looking for a Machine Learning Engineer to design, develop, deploy, and maintain production-grade machine learning and AI systems. The role requires strong Python, ML fundamentals, and experience taking models from experimentation to production.

Responsibilities:

- Build and train machine learning and deep learning models.

- Develop end-to-end ML pipelines for training, evaluation, deployment, and monitoring.

- Work with structured, unstructured, image, or text datasets.

- Deploy scalable ML inference APIs and services.

- Optimize models for latency, accuracy, scalability, and cost.

- Implement model monitoring, versioning, retraining, and MLOps workflows.

- Collaborate with backend, data, product, and engineering teams.

- Evaluate new ML/GenAI technologies and integrate them where appropriate.

Tech Stack:

- Languages: Python

- Frameworks: PyTorch, TensorFlow

- APIs: FastAPI, Flask

- Infrastructure: AWS, Azure, GCP, Docker, Kubernetes

- Tools: SQL, Git, Linux, MLflow

Required Qualifications:

- 3 - 6 years of software engineering / machine learning experience.

- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, or a related field.

Preferred Qualifications:

- Experience building production-scale AI systems.

- Experience with vector databases and distributed systems.

- Experience optimizing model inference using ONNX, TensorRT, vLLM, or similar technologies.

- Familiarity with transformers, embeddings, LLMs, RAG, or Generative AI is a plus.