Lead Engineer - Gen AI

Target Target · Retail · Bangalore, India

Lead Engineer role focused on integrating GenAI and LLMs into core applications, building agentic frameworks, and improving AI reliability. Requires strong backend engineering skills in Java/Python, microservices, and cloud platforms, with an emphasis on building scalable and resilient AI-powered systems.

What you'd actually do

  1. Designing scalable architecture with the best choice of tech, responsible for all the services/functionalities that the team develops while ensuring quality of the team's code and/or infrastructure standards.
  2. Hands-on development, often taking on the more complicated tasks. Ensures solution is production ready, deployable, scalable and resilient.
  3. Planning and delivering of work in the team in addition to their own work. Promotes a learning culture through mentoring and coaching.
  4. Ensures product observability is in place for reliability. Fosters a culture of observability across teams and helps use operational data to improve stability and performance of their domains. Drives monitoring work on their team based on the organization's monitoring philosophy. Is aware of the operational data for their team’s domain and uses it as a basis for driving changes to the team's services to achieve stability and performance improvements.
  5. Responsible for ensuring the security of the product and fostering a security first mindset across teams. Highly skilled with applying and implementing security concepts such as identifying vulnerabilities in software, creating logic to detect malicious behavior, and analyzing network or host artifacts.
  6. Integrate Large Language Models (LLMs) and GenAI-powered features into core applications.
  7. Build robust systems that mitigate hallucinations and improve AI reliability.
  8. Experience building agentic frameworks and AI-driven automation for process enhancement

Skills

Required

  • Java
  • Python
  • Microservices Architecture
  • Spring Boot
  • Micronaut
  • AI / LLM Integration
  • AI Reliability & Automation
  • OpenAI APIs and SDKs
  • Kafka
  • RabbitMQ
  • Cassandra
  • MongoDB
  • PostgreSQL
  • CI/CD
  • Jenkins
  • GitLab
  • Unit and Integration Testing
  • Spock
  • JUnit
  • TestContainers
  • PyTest
  • Unittest
  • AWS
  • GCP
  • Azure
  • Docker
  • Kubernetes
  • Monitoring & Observability
  • Grafana
  • ELK Stack
  • Prometheus
  • Event-Driven Architecture

Nice to have

  • Functional Programming
  • Kotlin
  • GraphQL
  • Legacy System Modernization
  • Security Best Practices
  • OWASP
  • vulnerability scanning
  • secure coding principles
  • Agile Methodologies
  • Scrum
  • Kanban

What the JD emphasized

  • scalable architecture
  • production ready
  • deployable
  • scalable
  • resilient
  • observability
  • stability
  • performance
  • security
  • GenAI
  • AI reliability
  • agentic frameworks
  • AI-driven automation

Other signals

  • Integrate Large Language Models (LLMs) and GenAI-powered features into core applications.
  • Build robust systems that mitigate hallucinations and improve AI reliability.
  • Experience building agentic frameworks and AI-driven automation for process enhancement