Software Engineer II — AI Transformation Engineering

Abnormal AI Abnormal AI · Vertical AI · Bangalore, India · Hybrid · Gen AI

Software Engineer II on the AI Transformation Engineering team to build and extend the internal AI platform (Nora). This role involves shipping services, factories, and sandboxes for internal teams to deploy AI workflows, integrating AI into existing internal systems (Salesforce, Jira, data warehouses), and standing up net-new infrastructure. The focus is on building platforms that others build on, designing for handoff, and owning measurable business outcomes.

What you'd actually do

  1. Build the Nora platform — Ship the services, factories, and sandboxes that internal teams use to deploy AI workflows. Own real platform components end-to-end.
  2. Weave AI into our internal systems — Build the connectors, services, and integrations that bring AI into the record-keeping and operational systems Abnormal already runs on (Salesforce, Jira, data warehouses, internal apps). The hard problem is making AI useful where work actually happens.
  3. Stand up net-new infrastructure — When a transformation requires capabilities that don't exist yet, you build them. Spec, ship, and harden the platform so other teams can build on top.
  4. Design for handoff — Build the platform, then transition long-term ownership to a permanent home (often a platform team in R&D). Write the docs, define SLOs, and pick the next owner before you ship.
  5. Own outcomes, not just output — Every engineer on this team owns a measurable contribution to a business outcome — revenue, cost saved, hours returned to the business, or capabilities unblocked for the teams building on top of you.

Skills

Required

  • 3+ years of professional experience in software development
  • Strong backend experience with Python
  • Fluency with the AI development stack — Claude, Gemini, Anthropic APIs, OpenAI APIs, and the surrounding tooling (APIs, MCP, agents)
  • Experience building scalable, enterprise-grade applications
  • Knowledge of cloud platforms (AWS, GCP, or Azure)
  • Knowledge of containerization (Docker, Kubernetes)
  • Strong fundamentals in computer science, distributed systems, and performance optimization

Nice to have

  • building plugins, skills, or integrations against AI APIs

What the JD emphasized

  • shipping software that moves real business numbers
  • The hard problem is making AI useful where work actually happens.
  • shipping software that moves real business numbers
  • track record of shipping platforms others build on
  • Operator empathy
  • A bias for handoff

Other signals

  • building internal AI platform
  • shipping AI workflows
  • integrating AI into existing systems