LLM Development & Optimization
- Develop, fine-tune, and optimize large language models using frameworks like PyTorch, TensorFlow, JAX, or Hugging Face.
- Implement prompt engineering, structured prompting, and retrieval-augmented generation (RAG) strategies.
- Evaluate and improve model performance through experimentation, benchmarking, and A/B testing.
- Design tools and pipelines for inference optimization, quantization (e.g., GGUF/INT4), and model compression.
Machine Learning Engineering
- Build and deploy end-to-end ML workflows, including data preprocessing, model training, validation, monitoring, and versioning.
- Implement scalable ML services using APIs, microservices, or model-serving frameworks (e.g., FastAPI, Triton, BentoML, Ray Serve).
- Develop automated pipelines for continuous training (CT), continuous evaluation (CE), and MLOps best practices.
RAG & Data Layer Integration
- Architect and implement RAG pipelines using vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS).
- Integrate retrieval systems with operational stores, object storage, and data warehouses.
- Build data governance, redaction, and quality mechanisms for structured, semi-structured, and unstructured data.
Agent Frameworks & Workflow Automation
- Build AI agents capable of tool execution, multi-step reasoning, and orchestration across systems.
- Integrate LLMs with external tools, APIs, and event-driven workflows.
- Collaborate with product and engineering teams to embed AI-driven decisioning within enterprise systems.
Research & Innovation
- Stay updated with advancements in LLMs, multi-modal models, distillation, and efficient training techniques.
- Prototype new capabilities, evaluate emerging models, and recommend architectural improvements.
