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Entry LevelFull-timePythonJavaKubernetesDockerGitAIMLNLP
Salary Range
$10,200 - $13,800 /year
This salary is estimated based on similar roles. The actual salary may vary.
Key Responsibilities
- →Design and implement GenAI and Agentic AI applications for complex document understanding, reasoning, and decision support
- →Build and optimize RAG pipelines tailored to regulatory and legal documents with high precision and grounded responses
- →Develop robust document ingestion and retrieval strategies including contextual chunking, embeddings, metadata enrichment, and semantic indexing
- →Implement reference identification, citation tracking, and traceability mechanisms for document-centric AI workflows
- →Optimize retrieval ranking, semantic search, and grounding to improve answer accuracy and reduce hallucinations
- →Integrate Knowledge Graphs with LLM workflows for structured and unstructured reasoning
- →Orchestrate multi-step AI workflows using LangChain, LangGraph, or similar agent frameworks
- →Establish AI quality assurance and evaluation practices including retrieval evaluation, hallucination detection, LLM judge frameworks, and RAGAS-style scoring
- →Build, train, and fine-tune specialized NER and document understanding models
- →Ensure explainability, auditability, and compliance of AI outputs in regulated environments
- →Support end-to-end model lifecycle activities including experimentation, versioning, deployment readiness, and monitoring handover
Requirements
- ✓4–8 years of experience as an AI Engineer
- ✓Strong emphasis on reference identification, citation grounding, retrieval quality, traceability, explainability, and evaluation in document-centric AI systems
- ✓Experience with legal, regulatory, compliance, and policy documents
- ✓Understanding of requirements around auditability, explainability, and risk controls in AI systems
