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Salary
≈ $24k – $53k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff

First seen by Alion on Oct 2, 2026.

Overview
Company
Impact
Profile match
DataZymes is a Data Science company driving digital-led innovation specializing in Marketing Analytics, Sales Analytics, Payer Analytics, Provider Analytics, Access Analytics, Patient Analytics, etc.

Responsibilities:

  • Design and implement complex components of agentic pipelines, multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems using LangGraph, AutoGen, CrewAI, or equivalent.
  • Take ownership of full sub-system designs: define agent topology, data flows, API contracts, and failure handling for a bounded scope.
  • Build and optimise production RAG pipelines: document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.
  • Integrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.
  • Own observability for components: instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.
  • Lead CI/CD for owned modules: containerisation (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures.
  • Translate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications.
  • Apply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component.
  • Build intelligent document processing pipelines for pharma content: drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.
  • Contribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies.
  • Serve as the day-to-day technical reference for AI Engineers on the pod: code review, design feedback, unblocking implementation issues.
  • Lead component-level design reviews and surface architecture risks before they reach staging.
  • Pair with junior engineers on hard problems and document patterns and decisions in the team's shared knowledge base.
  • Represent engineering quality in client-facing technical discussions and translate complex trade-offs into plain language.
  • Contribute reference implementations and guardrail templates to the firm's internal agentic AI playbook.
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