Join our AI/ML team to build and deploy generative and traditional ML models--from ideation and data preparation to production pipelines and performance optimization. You'll solve real problems, handle data end-to-end, navigate the AI development lifecycle, and contribute to both model innovation and operational excellence.
Key Responsibilities:
? Full AI/ML Lifecycle: Engage from problem scoping through data collection, modeling, deployment, monitoring, and iteration.
? Generative & ML Models: Build and fine-tune transformer-based LLMs (like GPT, BERT) both commercial as well as local, GANs, diffusion models; also develop traditional ML models for classification, regression, etc. Experience with DL models for Computer vision like CNN, R-CNN, etc is a plus.
? Data Engineering: Clean, label, preprocess, augment, and version datasets. Build ETL pipelines and features for model training. Experience with libraries like pandas, numpy, nltk, etc.
? Model Deployment & MLOps: Containerize models (Docker), deploy APIs/microservices, implement CI/CD for ML, monitor performance and drift .
? Troubleshooting & Optimization: Analyze errors, handle overfitting/underfitting, hallucinations, class imbalance, latency concerns; tune model performance.
? Collaboration: Partner with project managers, DevOps, backend engineers, and senior ML staff to integrate AI features.
? Innovation & Research: Stay current with GenAI (prompt techniques, RAG, LangChain, LLM models), test new architectures, contribute insights.