AI Engineer
HCLTech
Skills & Eligibility
- To ensure matching benchmarks for this advanced innovation unit, candidates must fulfill the following institutional and academic profiles:
- Degree Requirement: Must be pursuing a full-time Bachelor of Engineering (B.E.) or Bachelor of Technology (B.Tech) degree in a matching computer intelligence or circuit-related field.
- Target Year: Open strictly to students graduating in the active 2026 Batch .
- Mandatory Institute Affiliation: Applications will only be verified for candidates studying at premier designated campuses: Indian Institutes of Technology (IITs), National Institutes of Technology (NITs), Indian Institutes of Information Technology (IIITs), Delhi Technological University (DTU), BITS Pilani, and Netaji Subhas University of Technology (NSUT).
- As an AI Engineer in the advanced analytics division, you will contribute directly to configuring production-ready multi-agent environments. Ideal candidate profiles must demonstrate matching skills across the following vectors:
- LLM Solution Development: Practical project experience designing, training, and building Large Language Model applications (such as advanced cognitive conversational bots, multi-document summarizers, and tailored recommendation setups) within hyperscaler cloud cloud environments like Azure or Google Cloud Platform (GCP).
- Retrieval-Augmented Architecture: Strong structural understanding of Retrieval-Augmented Generation (RAG) workflows, prompt engineering methodologies, context tuning, and embedding databases.
- Framework Orchestration: Active exposure or concrete academic project integration across toolkits such as Microsoft AI Foundry, Azure AI Services, LangChain, Semantic Kernel, AutoGen, or CrewAI.
- Agentic AI Engineering: Clear conceptual foundation in multi-agent orchestration paradigms, with foundational execution knowledge in configuring autonomous agent workflows or proof-of-concepts (POCs).
- Operational Business Evaluation: Analytical mindset to assess technical penetration points within classic IT infrastructure systems and formulate return on investment (ROI) structures for proposed automation tracks.
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