Sr Engineer -advanced AI

Target Target · Retail · Bangalore, India

Senior AI Engineer role focused on building, deploying, and maintaining end-to-end AI/ML systems, including LLM-powered and agentic applications, for enterprise business value. Requires strong software engineering skills, experience with AI/ML frameworks, and understanding of production AI system design and deployment.

What you'd actually do

  1. help build, deploy, and maintain AI/ML applications that support automation, insight, and action across core business workflows
  2. work closely with Data Scientists, engineers, product partners, platform teams, security teams, and business stakeholders to turn well-defined and moderately ambiguous business problems into practical technical solutions
  3. contribute hands-on to the development of production-grade AI applications
  4. write maintainable, well-tested code, support model and framework integration, build APIs and services, develop data and application workflows, and contribute to deployment, monitoring, documentation, and production support
  5. help ensure AI applications are secure, reliable, maintainable, and aligned to Target’s enterprise standards for infrastructure, platform architecture, data handling, and operational readiness

Skills

Required

  • 3+ years of hands-on software engineering experience, including experience building or supporting production systems
  • Experience developing AI/ML applications, including LLM-powered applications, applied machine learning solutions, data-intensive applications, intelligent automation capabilities, or agentic systems
  • Strong proficiency with Python and experience with AI/ML or deep learning frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, Semantic Kernel, or similar tools
  • Experience working with model APIs, prompt orchestration, retrieval-augmented generation, evaluation approaches, observability tools, cloud platforms, containers, or orchestration technologies
  • Understanding of system design, application architecture, model and framework tradeoffs, experimentation, evaluation, performance optimization, and production deployment considerations for AI systems
  • Experience building maintainable and well-tested services, APIs, data pipelines, applications, or platforms
  • Experience with version control, CI/CD, code review practices, documentation, operational monitoring, and production support
  • Ability to break down business and technical requirements into clear engineering tasks and deliver practical solutions in collaboration with cross-functional partners
  • Strong communication skills, with the ability to explain technical concepts clearly to engineers, Data Scientists, product partners, and business stakeholders
  • Ability to work independently on defined workstreams, collaborate effectively with senior technical partners, and contribute to strong engineering practices within the team
  • Self-driven and results-oriented, with strong ownership, sound judgment, and the ability to move quickly while maintaining high technical standards
  • Collaborative team player with a commitment to continuous learning, knowledge sharing, and building reliable AI systems that create business value

What the JD emphasized

  • AI/ML applications
  • LLM-powered applications
  • agentic systems
  • Python
  • PyTorch
  • TensorFlow
  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • model APIs
  • prompt orchestration
  • retrieval-augmented generation
  • evaluation approaches
  • observability tools
  • cloud platforms
  • containers
  • orchestration technologies
  • system design
  • application architecture
  • model and framework tradeoffs
  • experimentation
  • evaluation
  • performance optimization
  • production deployment considerations for AI systems
  • maintainable and well-tested services
  • APIs
  • data pipelines
  • applications
  • platforms
  • version control
  • CI/CD
  • code review practices
  • documentation
  • operational monitoring
  • production support
  • break down business and technical requirements
  • deliver practical solutions
  • communication skills
  • explain technical concepts clearly
  • work independently
  • collaborate effectively with senior technical partners
  • contribute to strong engineering practices
  • Self-driven
  • results-oriented
  • ownership
  • sound judgment
  • move quickly
  • maintaining high technical standards
  • Collaborative team player
  • continuous learning
  • knowledge sharing
  • building reliable AI systems
  • create business value

Other signals

  • build, deploy, and maintain AI/ML applications
  • LLM-powered applications
  • agentic architectures
  • production-grade AI applications