Description

You will design and build Generative AI systems and implement core machine learning solutions.

Responsibilities

• Architect and deploy advanced RAG and GraphRAG applications.
• Develop and maintain traditional ML models (Regression, Classification, Clustering, Time-Series).
• Build multi-agent systems using frameworks like LangGraph or AutoGen to automate analysis.
• Implement evaluation frameworks (Ragas, DeepEval) to benchmark LLM performance and standard metrics for classic models.
• Write production-ready code in Python/TypeScript and manage CI/CD pipelines for scalable deployment via Docker and Kubernetes.

Required Skills

• 3-5 years of professional engineering experience.
• Expert proficiency in Python.
• Proven experience applying traditional ML techniques (Random Forest, XGBoost, Linear/Logistic Regression).
• Deep hands-on experience with LLM orchestration (LangChain, LlamaIndex).
• Production experience with Vector Databases (Pinecone, Weaviate, Chroma) and Hybrid Search.
• Solid experience with GCP/AWS/Azure, Docker, and Git.
• Experience with Prompt Engineering techniques (CoT, ReAct) and structured outputs.
• Experience with Knowledge Graphs (Neo4j) or GraphRAG implementations.
• Familiarity with Transformers, Embedding, Feature Engineering, and Model Evaluation fundamentals.

Key Skills

Education

Any Gradute