Tech companies have shifted focus from basic AI experimentation to building production-ready, enterprise-grade autonomous systems. Employers actively prioritize candidate capabilities in scaling, optimizing, and governing AI systems safely.
- Agentic Workflows: Building autonomous multi-agent systems using LangChain, AutoGen, and CrewAI to execute complex, multi-step tasks.
- MLOps & Deployment: Operationalizing and scaling models in cloud environments with Docker, Kubernetes, and MLflow to control costs and latency.
- Model Fine-Tuning: Customizing open-source LLMs (e.g., Llama, Mistral) on proprietary enterprise data using LoRA, PEFT, and Hugging Face.
- Vector Search & RAG: Indexing unstructured data with vector databases like Pinecone and Qdrant to ground model outputs and stop hallucinations.
- AI Safety & Security: Hardening endpoints against prompt injection, auditing bias, and enforcing governance with tools like NeMo Guardrails.
- AI Evaluation: Automated benchmark-testing of probabilistic model logic and reliability using frameworks like Ragas and DeepEval.
- Full-Stack AI Integration: Wiring custom model endpoints and APIs directly into enterprise codebases using Python, FastAPI, and Node.js.
