Job Title: AI Research Engineer
Job Mode: Hybrid

Experience: 5+ Years
Location: Bengaluru, India
Employment Type: Permanent

Notice Period: Immediate Joiner

Role Purpose

The AI Research Engineer is responsible for developing AI features and architecting scalable machine learning systems. The role focuses on building LLM, computer vision, and multimodal ML pipelines, deploying models into production, and ensuring reliability and cost efficiency.

Key Responsibilities

- Design and develop AI features from data ingestion to real-time inference for production applications.
- Architect scalable, cost-efficient, and observable ML systems and services.
- Build LLM, computer vision, or multimodal ML training pipelines, including fine-tuning, adapters, retrieval, and evaluation.
- Define model evaluation frameworks using offline metrics, live A/B testing, and user feedback.
- Collaborate with Software Engineering, Data Engineering, and Product teams to develop ML-powered features.
- Deploy and monitor models using containerized microservices on ECS and Kubernetes.
- Manage incident response and postmortem analysis for AI systems to improve reliability and reduce MTTR.
- Optimize training and inference costs using batching, quantization, mixed precision, and GPU scheduling.

Required Qualifications

Education





- Bachelor's or Master's degree from a Tier-1 institute (IIT or equivalent) in Computer Science, Artificial Intelligence, or a related field.

Required Technical Skills

- Robust proficiency in Python.
- Hands-on experience with PyTorch or TensorFlow.
- Experience with end-to-end ML pipelines, including data preparation, training, evaluation, deployment, and monitoring.
- Experience delivering ML-powered products in production.
- Hands-on experience with MLOps tools such as MLflow, Weights & Biases, DVC, Airflow, or Prefect.
- Knowledge of Docker, Amazon ECS, Kubernetes, and CI/CD for ML deployments.
- Strong understanding of statistics, experimentation, and model evaluation.
- Experience optimizing GPU inference using ONNX, TensorRT, mixed precision, and batching.
- Experience with vector databases, Retrieval-Augmented Generation (RAG), and LLM fine-tuning using LoRA or QLoRA.
- Familiarity with observability tools such as Prometheus, Grafana, OpenTelemetry, and production monitoring.

Success Indicators

- Successful delivery of scalable AI features for production systems.
- Reliable deployment and monitoring of ML models.
- Efficient optimization of model training and inference.
- Development of production-ready AI solutions with measurable performance and reliability.

📌 Artificial Intelligence Research Engineer (Bengaluru)
🏢 Experis
📍 Bengaluru