A
Altimetrik
Claude Transformation Engineer
Skills
NodeTypescriptPythonGraphQLAICompliance
25 days ago
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Important information
Contract type:
Permanent
Salary:
₹50,000 – ₹300,000 / year
Location:
Bengaluru, Karnataka, India
Work mode:
Hybrid
Published on:
30 September 2026
What they need
Required Qualifications
- 8+ years of overall experience, including at least 3+ years in software engineering, solutions engineering, or a technical transformation/automation role, ideally within payments, fintech, banking, or another regulated financial environment.
- Hands-on experience building with LLMs in production - direct experience with the Claude API (or comparable experience with GPT/Gemini APIs and a strong willingness to specialize in Claude) including tool use, structured outputs, and context/prompt engineering.
- A demonstrated track record of scaling technical tool adoption across a large organization. This matters more than payments domain knowledge. Evidence of designing enablement programmes, champion networks, training curricula, or adoption metrics, not just shipping one tool for one team.
- Strong software engineering fundamentals: proficiency in Python and/or TypeScript/Node.js, REST/GraphQL API design, and experience integrating third-party APIs into production systems.
- Practical understanding of data security and compliance constraints relevant to financial services (PCI DSS, data minimization, access controls, audit logging).
- Excellent stakeholder communication - able to explain AI capabilities and limits clearly to both engineers and non-technical operations/risk stakeholders.
Preferred Qualifications
- Experience running an internal AI adoption or AI Center of Excellence function at scale, ideally with a visible track record similar to enablement-led (rather than build-led) transformation programmes.
- Experience with agentic workflows, RAG architectures, or MCP (Model Context Protocol) integrations.
- Familiarity with Claude Code, Claude in various IDEs, or other AI-assisted development tooling.
- Experience with evaluation frameworks for LLM output quality, safety, and reliability.
- Familiarity with commit-data-driven visibility into engineering delivery.
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