AI Application engineer
About the Role:AI Application engineer who understands data as well as application ; primary databricks and secondary snowflake DescriptionKey ResponsibilitiesDesign and develop AI‑powered applications using machine learning, generative AI, and data‑driven services.Integrate ML models, LLMs, and AI services into web, mobile, and enterprise applications.Build production‑grade APIs and microservices to serve AI predictions and insights.Collaborate with data scientists and ML engineers to operationalize models.Implement prompt engineering, model orchestration, and inference pipelines.Ensure performance, scalability, security, and reliability of AI applications.Work on real‑time and batch AI inference use cases.Implement observability and monitoring for AI behavior and application health.Handle model versioning, rollback strategies, and A/B testing.Ensure compliance with data privacy, responsible AI, and governance standards.Participate in architecture reviews and contribute to AI application best practices.Troubleshoot application and inference issues in production environments.Mentor junior developers and contribute to technical documentation.Required Skills & QualificationsApplication Development5–6 years of experience in application development or software engineering.Strong proficiency in Python and/or JavaScript/TypeScript.Experience with backend frameworks (FastAPI, Flask, Django, Node.js).Strong understanding of REST APIs, microservices, and system design.Experience with frontend frameworks is a plus (React, Angular, Vue).AI & Machine LearningHands‑on experience integrating ML models and AI services into applications.Understanding of ML lifecycle, inference patterns, and model usage.Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Hugging Face).Knowledge of prompt engineering, context management, and RAG (Retrieval‑Augmented Generation).Familiarity with embeddings and vector search.Data & Backend IntegrationStrong SQL skills and experience with databases (relational & NoSQL).Experience integrating with data pipelines, feature stores, and analytics systems.Knowledge of APIs, caching layers, and messaging systems (Kafka, RabbitMQ).Cloud & DevOpsHands‑on experience with cloud platforms (AWS / Azure / GCP).Experience deploying AI applications using Docker & Kubernetes.Familiarity with CI/CD pipelines.Experience with cloud‑native AI services is a plus.