The role spans classical machine learning, generative AI, and agentic AI systems, focused on building scalable, production-grade AI solutions.

• Design, build, and deploy ML models for classification, clustering, information extraction, and entity-level understanding
• Perform feature engineering, model selection, and hyperparameter optimization using modern tuning frameworks
• Conduct in-depth Exploratory Data Analysis (EDA) to guide modeling strategy and detect data anomalies

• Develop and optimize agentic AI workflows using RAG, LangChain, LangGraph, or similar frameworks
• Build advanced LLM-based pipelines including retrieval, memory, evaluation, and tool-using agents
• Fine-tune LLMs, embedding models, and domain-specific architectures for production use
• Design intelligent orchestration across agents, microservices, and external APIs

• Implement NLP solutions for text segmentation, summarization, semantic search, and entity extraction
• Design advanced chunking strategies for long and complex documents (tables, scanned PDFs, multi-column layouts)
• Integrate and optimize vector databases for high-performance embedding retrieval

• Architect AI/ML pipelines using microservices, vector stores, message queues, and event-driven systems
• Build scalable distributed systems using cloud-native services (Cloud Run, Pub/Sub, Cloud Functions, etc.)
• Develop robust inference pipelines with streaming, batching, monitoring, and automated evaluation

• Strong background in classical ML, statistical modeling, and hyperparameter optimization
• Solid expertise in NLP, embeddings, and document intelligence
• Hands-on experience with vector databases (Pinecone, Weaviate, Milvus, Chroma, etc.)
• Proven experience designing scalable microservices and event-driven architectures
• Deep understanding of LLMs, RAG systems, embeddings, and agentic AI frameworks
• Strong Python skills (PyTorch or TensorFlow preferred)
• Experience deploying production workloads on cloud platforms (GCP preferred)