DESCRIPTION

WHAT YOU’LL DO
LLM Engineering Standards Across Agent Pods
Define and own LLM engineering standards across all Agent Stream pods —
agent framework conventions, prompt lifecycle standards, eval harness
design patterns, guardrail implementation, and confidence threshold
calibration methodology that all Senior AI/ML Engineers follow.
Own the Langfuse observability framework at platform level —
instrumentation standards, trace validation patterns, eval pipeline design,
prompt regression testing, and model version regression detection.
Pod-level Langfuse usage is consistent because this role defines how it is
done.
Standardise RAG pipeline architecture across pods — embedding strategy,
vector database selection and management, retrieval strategy, reranking,
and structured output design for financial document reasoning. Shared RAG
infrastructure is your design.
Review and challenge agent design proposals from pod AI/ML engineers —
raise the bar on prompt design, memory architecture, evaluation rigour, and
production reliability across the stream.




Contribute to AI/ML hiring — define the technical bar for AI/ML engineers
across the stream, participate in interviews, and ensure hiring standards
are consistent across pods.
Memory Architecture Ownership
Own the memory architecture strategy for the AI Platform — designing the
personalised, complex memory layer that agents depend on for continuity,
context, and adaptive behaviour across sessions and users.
Design and implement the tiered memory architecture for agent workflows —
working memory (in-context), episodic memory (past interactions), semantic
memory (extracted facts and preferences), and procedural memory (agent
instruction updates). Select and implement the right memory framework for
each tier: LangMem for LangGraph-native flows, Mem0 for managed
personalisation, Zep/Graphiti for temporal and knowledge-graph reasoning,
or Letta for explicit OS-style memory management.
Define memory h

📌 Principal Ai/ml Engineer Lead Bengaluru
🏢 Trintech
📍 Bengaluru