AI Engineer

About The Opportunity

A fast-scaling AI innovation hub operating at the intersection of enterprise automation and generative AI, we build end-to-end intelligent systems that power real-time decision-making, automated content generation, and scalable LLM pipelines for global clients. Our engineers ship production-grade AI models—from fine-tuning open-source LLMs to deploying RAG architectures and agent-based workflows—on cloud-native stacks with a focus on performance, observability, and compliance.

Role & Responsibilities

• Design, train, and deploy generative AI models (LLMs, fine-tuned transformers) for enterprise use cases including summarization, classification, and multi-agent orchestration.
• Build RAG pipelines with vector databases (FAISS, Pinecone) and integrate retrieval-augmented workflows into production APIs using LangChain/LangGraph.
• Optimize inference latency and cost by quantizing models, implementing caching layers, and deploying on Kubernetes (AKS/EKS) with autoscaling.
• Collaborate with ML engineers and data scientists to automate data preprocessing, model versioning, and drift detection in MLOps pipelines.
• Implement model monitoring, bias detection, and explainability tools to ensure ethical AI deployment compliant with internal governance standards.
• Document architecture decisions, maintain engineering runbooks, and mentor junior engineers on best practices for scalable AI systems.
  
  

• Python
• PyTorch
• LangChain
• Transformers
• FAISS
• LLM fine-tuning
• RAG architecture
• Kubernetes
  
  

• LangGraph
• Pinecone
• MLflow
  
  

• Work on bleeding-edge AI projects with real global business impact—no legacy tech or maintenance grind.
• Direct mentorship from principal AI architects and access to cutting-edge research papers + model stacks.
• On-site collaborative culture with innovation labs, hackathons, and quarterly tech showcases.