Lead Software Engineer – AI Automation
Build and ship production-grade AI automations that combine Python, REST APIs, data stores and Large Language Models to transform enterprise operations at scale.
Who Is This Role For?
This role is designed for a strong production-focused software engineer who can turn business process specifications into reliable, monitored and scalable AI-powered automation systems.
Production Python Engineer
Experienced in building Python systems that run in production, handle failures, integrate external services and evolve through continuous delivery.
AI Automation Engineer
Comfortable integrating LLMs into real workflows using prompts, structured outputs, validation and quality thresholds.
Integration-Focused Engineer
Strong with REST APIs, authentication, rate limits, pagination, data stores, SQL and troubleshooting complex integrations.
Agentic AI Builder
Interested in designing multi-step AI workflows involving tools, memory, planning, orchestration and evaluation.
Build Automation That Works in Production
The role sits within Circana's AI & Automation transformation program. Engineers take process specifications and convert them into deployed, monitored systems that deliver measurable operational improvements.
What You Will Do
Translate Process Specifications Into Technical Solutions
Design technical approaches covering data flow, integrations, LLM components, error handling and human-in-the-loop controls.
Build Python-Based Automations
Develop production automations involving REST APIs, data stores, transformation logic, LLM integrations and workflow orchestration.
Integrate Large Language Models
Implement prompt engineering, structured output parsing and validation against defined quality and acceptance thresholds.
Build Quality Into AI Automations
Create tests covering core logic, external dependencies and LLM output quality to ensure reliable production behavior.
Deploy and Stabilize Solutions
Deploy solutions into production, monitor initial runs and manage hypercare until the automation reaches stable operation.
Build Reusable Engineering Patterns
Contribute reusable components and patterns to the shared automation library across multiple projects.
Develop Full-Stack Automation Tools
Build lightweight React and FastAPI interfaces for review queues, exception dashboards, approvals and configuration.
Develop Agentic Workflows
Contribute to multi-step AI workflows involving tool use, memory, planning and evaluation frameworks.
Support Agile Delivery
Participate in sprint planning, standups, reviews and retrospectives while identifying blockers early.
Enable Operations Teams
Hand over completed automations with documentation that non-engineering users can understand and follow.
Build Reliable LLM-Powered Automations
Prompt Engineering
Design prompts that produce predictable, useful and structured outputs for business workflows.
Structured Outputs
Parse and validate LLM responses against predefined schemas and business requirements.
Output Validation
Treat hallucination and unreliable AI output as engineering problems requiring explicit quality controls.
AI Quality Mindset
The role goes beyond simply calling an LLM API. Production AI automation requires validation, error handling, acceptance criteria, monitoring and clear human escalation paths.
Connect Systems Reliably
REST APIs
Build reliable API integrations with authentication, pagination, rate limiting, retries and error handling.
Integration Debugging
Diagnose integration failures and understand system behavior even when documentation is incomplete.
Data Integration
Connect automation workflows with relational databases, cloud storage and enterprise data platforms.
Work With Enterprise Data Stores
The engineer should be comfortable navigating data structures, querying data and integrating enterprise data stores into automation workflows.
Build the Next Generation of Intelligent Workflows
As the programme's agentic automation capabilities mature, the role contributes to workflows where AI can reason, use tools, maintain context and execute multi-step tasks.
Build Interfaces When Automation Needs Human Interaction
React
Create lightweight frontends for human review queues, approval workflows and automation configuration.
FastAPI
Build lightweight backend services and APIs that expose automation capabilities to human-facing applications.
Operational Dashboards
Create exception and monitoring interfaces that allow teams to review automation outcomes and intervene when required.
Production-Grade Development
Ask the Right Questions Before Building
Strong candidates do more than implement requirements. They examine process specifications critically, identify hidden assumptions and determine where an automation is most likely to fail.
Find Missing Information
Identify undocumented process steps, dependencies and assumptions before implementation begins.
Identify Failure Points
Anticipate where integrations, data, LLM outputs or business processes are most likely to break.
Design for Recovery
Build error handling, validation and human-in-the-loop mechanisms into the automation architecture.
Own the Outcome
Focus on whether the automation actually works for the business, not simply whether the code runs.
What You Should Bring
Skills & Technologies
Remote Opportunity
This position can be located remotely with Bangalore or Pune as the specified location base.
Build Production AI Automation
Turn complex business processes into reliable, scalable automations powered by Python, APIs, data and Generative AI.
Apply Now ↗