- Strong experience with RAG-based systems or Graph-based architectures

- Minimum 5 years of AI/ML experience

- Experience with MCP

- Prior work on end-to-end AI system development

AI System Development :

- Design and build production AI/ML systems, including agentic workflows, multi-model pipelines, and intelligent automation

- Develop and optimize LLM-based applications using prompt engineering, RAG, and fine-tuning techniques

- Build AI agent frameworks that orchestrate complex reasoning, tool usage, and multi-step workflows

ML Engineering & MLOps :

- Train, evaluate, and deploy ML models across classification, NLP, recommendation, and generative use cases

- Implement MLOps pipelines for model versioning, monitoring, A/B testing, and continuous improvement

- Optimize model performance, latency, and cost in production environments

Data & Infrastructure :

- Design vector databases, embedding systems, and semantic search architectures

- Build scalable data pipelines for training, inference, and continuous learning

- Create evaluation frameworks to measure AI system quality, safety, and performance

Technical & Engineering Skills :

- Strong programming skills in Python and ML frameworks such as PyTorch, TensorFlow, or JAX

- Hands-on experience with LLMs (GPT, Claude, Llama, etc.) and a clear understanding of their capabilities and limitations

- Proficiency with frameworks/tools such as LangChain, LlamaIndex, DSPy, Hugging Face, OpenAI API, Anthropic API

- Strong fundamentals in data structures, algorithms, and system design

- Experience building APIs, microservices, and distributed systems (FastAPI, Node.js, etc.)

- Cloud experience on AWS, GCP, or Azure, along with Docker and Kubernetes

- Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, Qdrant, and SQL/NoSQL databases