About the role
- Work
with LLMs, NLP models, neural networks, and other advanced AI
techniques.
- Build
scalable data pipelines and automate model training, validation, and
deployment workflows.
- Optimize
model performance, latency, and accuracy using modern techniques.
- Work
closely with cross-functional teams including Data Engineering, Product,
and Software Engineering.
- Perform
exploratory data analysis, feature engineering, and experiment tracking.
- Integrate
AI models into applications using APIs, microservices, or cloud-based
solutions.
- Stay updated with the latest advancements in AI,
ML, and GenAI technologies.
Requirements
- 10+ years of hands-on experience in AI/ML engineering.
- Strong
proficiency in Python, ML libraries (NumPy, Pandas, Scikit-learn),
and DL frameworks (TensorFlow/PyTorch).
- Experience
with LLMs, NLP, transformers, embeddings, and prompt optimization.
- Knowledge
of MLOps tools like MLflow, Kubeflow, Airflow, or similar.
- Experience
in using cloud platforms (AWS / Azure / GCP) for AI model
deployment.
- Hands-on
experience with vector databases, feature stores, or model
registries (nice to have).
- Familiarity
with API development, microservices architecture, and
containerization (Docker, Kubernetes).
- Strong
understanding of algorithms, data structures, and model evaluation
techniques.