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Bengaluru junior ML role with mid-level experience and broad LLM skillset yields medium candidate competition.
Core LLM and ML engineering skills transfer across industries, though legal-text specialization raises moderate domain specificity.
Many mandatory technical LLM, vector DB, Python, and evaluation skills plus explicit experience cap create high shortlisting strictness.
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Contribute to building and maintaining production AI systems for legal-tech SaaS products, focusing on AI features like document summarization, clause extraction, and multi-agent workflows.
Support retrieval-augmented generation (RAG) pipelines and semantic search systems using vector databases.
Assist in infrastructure tasks including API service development for LLM providers, inference optimization, evaluation scripting, and CI/CD pipeline support.
Up to 3 years of software or machine learning engineering experience with exposure to LLM-based systems.
Hands-on experience with LLM APIs such as GPT-4, Claude, Gemini, or Llama, including prompt engineering and function calling.
Proficient in Python with knowledge of core software engineering practices (testing, version control).
Foundational understanding of transformer architectures, embeddings, context engineering, and experience with vector databases and semantic search concepts.
Experience working with agentic AI workflows, chain of tool calls, and multi-step reasoning within AI systems.
Comfortable contributing in a fast-paced environment under mentorship from senior engineers, showing rapid learning ability.
Interest or background in MLOps practices (CI/CD, containerization, monitoring), legal-tech domain knowledge is a plus but not explicit.