AI/ML Engineer

Roles & Responsibilities • Design, develop, and deploy machine learning, deep learning, and NLP models for real-world business problems • Build and fine-tune LLM applications, including RAG pipelines, agents, and prompt engineering workflows • Develop end-to-end ML pipelines — data ingestion, preprocessing, feature engineering, training, evaluation, deployment • Integrate AI/ML models into production applications via APIs, in collaboration with data engineers and product teams • Optimize model performance, latency, and inference cost (including quantization) • Implement MLOps practices — CI/CD for ML, model versioning, experiment tracking, automated retraining • Work with vector databases and embedding models for semantic search and recommendation systems • Conduct model evaluation, A/B testing, and monitor for drift, bias, and performance degradation • Write clean, scalable, production-grade Python code • Research and evaluate emerging AI/ML tools and frameworks for business applicability • Mentor junior developers and contribute to code reviews and documentation • Ensure data privacy, security, and responsible AI practices throughout development • • Preferred Qualifications • Bachelor's/Master's degree in Computer Science, Data Science, AI/ML, or related field • Experience deploying AI agents or multi-agent systems (AutoGen, CrewAI, LangGraph) • Familiarity with model quantization and edge deployment • Contributions to open-source ML projects or published research is a plus • Understanding of responsible AI, model explainability (SHAP, LIME), and AI governance