Technical requirement:

Generative AI, Transformers & Predictive Deep Learning

- Transformer Engineering: Deploy, customize, and optimize open-source and proprietary Transformer models using the Hugging Face ecosystem (Transformers, Accelerate, PEFT) for custom downstream tasks.
- GenAI System Engineering: Architect production-grade agentic workflows, orchestrating LLMs using advanced framework pipelines integrated with high-performance Vector Databases (e.g., Pinecone, Milvus, Qdrant).
- Time-Series Deep Learning: Develop, train, and maintain sophisticated machine learning models (e.g., LSTMs, Temporal Fusion Transformers, DeepAR) to solve intricate sequential forecasting problems.
- Data & Feature Engineering: Design highly scalable multi-cloud data pipelines to feed both continuous time-series feature stores and unstructured text embedding pipelines.

Infrastructure Automation & CI/CD (IaC & MLOps)





•Zero-Touch Infrastructure: Design, provision, and maintain secure, reproducible multi-cloud environments using Terraform as the source of truth.

- Pipeline Automation: Architect, scale, and optimize high-throughput Continuous Integration and Continuous Deployment (CI/CD) pipelines via Jenkins and Azure DevOps to eliminate manual code, model weights, and data deployments.
- Production MLOps: Establish enterprise monitoring frameworks to track inference latencies, model data drift, token spending, fine-tuning schedules, and hallucination metrics.
- Container Orchestration: Manage elastic scaling of microservices and containerized AI training/evaluation workloads using Docker and Kubernetes (EKS, GKE, AKS).

📌 Artificial Intelligence Engineer (Gurugram)
🏢 EXL
📍 Gurugram