Engineer - Target India

Target; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Target in Bengaluru seeks an AI Engineer to transform our Device Management Platform with AI-powered capabilities at scale. You will design, develop, and deploy AI/ML solutions, collaborating with product, engineering, security, and infra teams to identify high-value use cases and deliver intelligent features.

Ideal candidates combine strong software engineering fundamentals with hands-on experience building and integrating AI/ML and Generative AI into production systems, including LLMs, RAG,

Qualifications

• 4+ years of software engineering experience with production-grade apps.
• Hands-on AI/ML experience including Generative AI in production.
• Experience with LLMs (OpenAI GPT, Claude, Gemini, Llama) and RAG.
• Familiarity with embeddings, vector databases, and semantic search.
• Understanding of model confidence, evaluation metrics, and safe fallbacks.
• Knowledge of CI/CD, testing, and observability practices.

Responsibilities

• Design, develop, and deploy AI-powered capabilities across the platform.
• Build intelligent search experiences using LLMs and semantic search.
• Develop data pipelines and APIs to support AI features.
• Collaborate with product, security, and infra teams for scalable AI adoption.
• Implement AI governance and responsible AI practices.

Skills

Python

Java

Kotlin

REST APIs

CI/CD

Observability

Problem solving

Tools

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Job description

We are looking for an AI Engineer to help transform our Device Management Platform through the adoption of intelligent, AI-powered capabilities. This role will focus on designing, developing, and integrating AI solutions that improve how enterprise devices are managed, monitored, and supported at scale. You will work closely with product, engineering, security, and infrastructure teams to identify high-value use cases and build solutions such as intelligent search, conversational assistants, anomaly detection, predictive insights, automated remediation, and AI-driven operational analytics. The ideal candidate combines strong software engineering fundamentals with practical experience building and integrating AI/ML and Generative AI solutions into production systems.

Core Responsibilities

AI Solution Development

• Design, develop, and deploy AI-powered capabilities across the Device Management Platform
• Build intelligent search experiences leveraging LLMs, semantic search, vector databases, and retrieval-augmented generation (RAG)
• Build and maintain MCP tools and integrations for device management, compliance, ITSM, etc.
• Develop AI-driven insights using device telemetry, operational metrics, and platform data
• Implement conversational experiences and AI assistants that simplify device management workflows
• Build AI features for compliance evaluation, anomaly detection, natural-language admin commands, and self-healing using language models and local RAG.
• Implement the confidence gateway that converts probabilistic AI decisions into deterministic actions with policy thresholds, guardrails, audit logs, and human-approval paths.

AI Platform Engineering & Systems Integration

• Develop scalable services and APIs that integrate AI capabilities into existing platform workflows
• Build data pipelines and retrieval systems to support AI applications
• Evaluate and integrate foundation models, LLMs, embeddings, and agent frameworks
• Design and implement prompt engineering, evaluation, and observability frameworks
• Ensure AI solutions are secure, reliable, scalable, and cost-effective
• Integrate AI capabilities with device management services, backend platforms, and enterprise systems
• Collaborate with platform teams to operationalize AI solutions in production environments
• Build and maintain CI/CD pipelines supporting AI applications and services
• Partner with infrastructure teams to optimize AI workloads and deployments

Architecture & Technical Leadership

• Identify opportunities to leverage AI to improve operational efficiency and user experience
• Establish best practices for AI solution design, model evaluation, governance, and responsible AI usage
• Contribute to architectural decisions around scalability, reliability, security, and AI adoption

Collaboration & Cross-Functional Impact

• Partner with product, engineering, security, infrastructure, and business teams to identify AI use cases and prioritize investments
• Translate business problems into AI-powered solutions
• Communicate technical concepts and AI recommendations to both technical and non-technical stakeholders
• Stay current with emerging AI technologies and bring innovative ideas into the platform

Required Qualifications

• 4+ years of software engineering experience, including experience building production-grade applications and services
• Hands-on experience developing and deploying AI-powered applications or Generative AI solutions
• Experience building applications using Large Language Models (LLMs) such as OpenAI GPT, Claude, Gemini, Llama, or similar models
• Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search
• Understanding of model confidence, evaluation metrics, false positives/negatives, and safe fallback behaviour.
• Familiarity with AI governance, explainability, and approval-gated automation for high-risk actions.
• Exposure to Anomaly detection, Confidence scoring and decision thresholds, Explainability and Auditability
• Strong proficiency in Python, Java, Kotlin, or a combination thereof
• Experience designing and implementing REST APIs and backend service integrations
• Experience working with cloud-based AI services and AI development frameworks
• Experience evaluating AI solutions and measuring quality through testing, experimentation, and monitoring
• Strong understanding of software engineering best practices, CI/CD, testing, and observability
• Strong problem-solving skills and ability to work across cross-functional teams

Preferred Qualifications

• Experience working with enterprise device management platforms, Android Enterprise, IoT systems, or edge devices
• Experience building intelligent search, recommendation engines, chatbots, or AI assistants
• Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar AI frameworks
• Experience leveraging device telemetry and operational data to build predictive analytics or anomaly detection solutions
• Familiarity with Temporal or similar workflow orchestration frameworks
• Experience with event-driven architectures and distributed systems
• Knowledge of responsible AI practices, model governance, and AI security