We are a $13+ billion global technology company, home to more than 224,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud, and AI, powered by a broad portfolio of technology services and products.
HCLTech is a globally recognized leader in the Tech and IT industry, but we’ve never forgotten the startup mindset that got us here. We’ve always approached our work with an idea-first attitude because every one of our accomplishments —no matter how big or small —can be traced back to an idea’s single spark.
It’s that spark —that inner drive —that sets our people apart from our competitors. It enables us not just to pull off game-changing feat after game-changing feat but to better our world in the process. We want you to find your spark. Because that’s what drives you to be better, be more and ultimately, be more fulfilled
Detailed JD :
Architect and deploy scalable AI systems leveraging LLMs (GPT, LLaMA, Claude, etc.)
• Build and fine-tune SLMs/LLMs using domain-specific data (e.g., ITSM, security, operations)
• Design and optimize Retrieval-Augmented Generation (RAG) pipelines with vector DBs (e.g., FAISS, Chroma, Weaviate, Pinecone)
• Develop agent-based architectures using LangGraph, AutoGen, CrewAI, or custom frameworks
• Integrate AI agents with enterprise tools (ServiceNow, Jira, SAP, Slack, etc.)
• Optimize model performance (quantization, distillation, batching, caching)
• Collaborate with DevOps and MLOps teams to build CI/CD pipelines for models and agents
• Conduct code and research reviews, mentor junior engineers, and contribute to technical strategy
• 5–8 years of hands-on experience in AI/ML/Deep Learning
• Strong coding skills in Python (must), familiarity with Node.js, Go, or Rust is a plus
• Proficient with PyTorch or TensorFlow
• Deep understanding of transformer architectures, embeddings, and attention mechanisms
• Experience with LangChain, Transformers (HuggingFace), or LlamaIndex
• Working knowledge of LLM fine-tuning (LoRA, QLoRA, PEFT) and prompt engineering
• Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Chroma)
• Cloud experience on Azure, AWS, or GCP (Azure preferred)
• Experience with Kubernetes, Docker, and scalable microservice deployments
• Experience integrating with REST APIs, webhooks, and enterprise systems (ServiceNow, SAP, etc.)
• Solid understanding of data pipelines, ETL, and structured/unstructured data ingestion