Job Summary The AI Engineer will be responsible for developing, deploying, and operationalizing machine learning and artificial intelligence models into production environments. This role requires solid expertise in MLOps best practices to ensure high system performance, scalability, monitoring, and seamless integration between models and software applications. Key Responsibilities

Model Development & Deployment: Build, train, test, and deploy AI/ML models (including GenAI, LLMs, NLP, CV, or classic ML algorithms) into enterprise production environments. Min 5 years of exp required MLOps & Pipeline Engineering: Establish and maintain robust CI/CD pipelines for ML models (MLOps), ensuring continuous integration, automated testing, version control, and seamless deployment.

Scalability & Performance: Optimize model latency, throughput, and resource utilization to ensure high system availability, scalability, and performance under production loads.

Model Monitoring & Maintenance: Set up monitoring solutions to track model health,



performance metrics, data drift, concept drift, and system latency post-deployment. Cross-Functional Collaboration: Partner closely with Data Engineers (for data ingestion/pipelines) and AI Data Scientists (for model validation and architecture) to translate experiments into production-grade systems.

Key Technical

Requirements & Skills

Languages: Python, C++, or Java (expert-level coding & modular software engineering skills).

ML/AI Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, or LlamaIndex.

MLOps & Containerization: Docker, Kubernetes, MLflow, Kubeflow, Airflow, or BentoML.

Cloud Platforms: Hands-on experience with AWS (SageMaker), Azure ML, or GCP (Vertex AI).

Databases & Architecture: REST/gRPC APIs, Vector Databases (Pinecone, Weaviate, Qdrant, Milvus), and microservices architecture.

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 Ariston Services
📍 Bengaluru