Senior ML Engineer

Description :

We are looking for a Senior Machine Learning Engineer to design, build, and deploy scalable machine learning and AI solutions that power intelligent products. In this role, you will work closely with product, engineering, and data teams to translate business problems into robust ML models and production-ready systems.

You will play a key role in developing LLM-driven solutions, deep learning models, and end-to-end ML pipelines, contributing to both research and real-world production deployments.

Key Responsibilities :

- Design, develop, and deploy machine learning and deep learning models for production use.

- Build and optimize models including :

1. Large Language Models (LLMs)

2. NLP models

3. Classification and prediction models

4. CNNs and Transformer-based architectures

- Fine-tune and adapt pre-trained LLMs for domain-specific use cases.

- Develop end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and deployment.

- Collaborate with backend and platform engineers to integrate ML models into APIs and products.

- Monitor model performance, drift, and accuracy; continuously improve models in production.

- Conduct experimentation, model benchmarking, and A/B testing.

- Ensure scalability, reliability, and performance of ML systems in cloud environments.

- Contribute to technical design discussions and mentor junior ML engineers.

Required Qualifications :

- 5+ years of hands-on experience in Machine Learning, Deep Learning, or Applied AI.

- Strong foundation in computer science, mathematics, probability, and statistics.

- Proven experience with :

1. Large Language Models (LLMs) and NLP

2. Classification models and predictive modeling

3. CNNs and Transformer architectures

- Proficiency in Python and ML frameworks such as PyTorch and/or TensorFlow.

- Experience working with cloud-based ML platforms (AWS, GCP, or Azure).

- Strong understanding of model lifecycle management, evaluation metrics, and deployment best practices.

- Excellent verbal and written communication skills with the ability to explain complex concepts clearly.

Preferred Qualifications :

- Experience in B2B SaaS, cybersecurity, or data platform products.

- Familiarity with MLOps practices including CI/CD for ML, model versioning, and monitoring.

- Experience deploying ML models as scalable APIs or microservices.

- Exposure to vector databases, embeddings, and retrieval-augmented generation (RAG).

- Experience with distributed training and large-scale datasets.