Sr Advanced AI Engineer

Key Responsibilities

• Design and develop AI application services and middleware that connect classic ML models, GenAI/LLM systems, and agentic AI components to enterprise applications and workflows.
• Build production-grade RAG (Retrieval-Augmented Generation) services, including chunking pipelines, embedding APIs, retrieval endpoints, caching, re-ranking, and content policy enforcement.
• Develop agent tool adapters and integration layers enabling AI agents to safely perform actions (e.g., Snowflake queries, workflow triggers, system updates) using secure, controlled APIs.
• Create policy, safety, and guardrail middleware that enforces PII protection, content moderation, compliance rules, and safe function execution for agentic systems.
• Implement event-driven and asynchronous services using AWS-native capabilities for agent orchestration, callbacks, monitoring, and workflow routing.
• Build microservices and SDKs that enable scalable, low-latency interactions between AI models, vector databases, and enterprise systems.
• Collaborate with AI Architects, Platform Engineers, MLOps, Data Engineers, and Data Scientists to ensure systems are reliable, secure, observable, and aligned with best practices.
• Implement robust testing frameworks for AI-driven services including regression tests, guardrail tests, prompt and agent behavior evaluations, and functional correctness checks.
• Participate in code reviews, architectural discussions, and continuous improvement initiatives to enhance the performance and reliability of AI-powered applications.

Qualifications

• Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering or mathematics.
• 5 or more years of experience in software development, ideally building backend, middleware, or distributed systems.
• Strong proficiency in Python or Java/TypeScript, with hands-on experience building REST APIs, event-driven services, and microservices.
• Experience working with AI/ML application patterns, including RAG, vector stores, prompt orchestration, and model/LLM integration.
• Practical experience deploying applications on AWS using services such as Lambda, API Gateway, ECS/EKS, Step Functions, SQS/SNS, or DynamoDB.
• Familiarity with Databricks (Delta Lake, SQL Warehouses, Model Serving, Vector Search) and integration patterns for data access and processing.
• Experience building data or AI workflows that interact with Snowflake, including secure queries, role-based access, and performance optimization.
• Experience working with Dataiku APIs or automation for data workflows, scoring endpoints, or integration into AI services.
• Strong understanding of API design, authentication/authorization, secure coding practices, and system observability.
• Ability to collaborate in an agile, cross-functional environment with platform engineering, MLOps, data engineering, and data science teams.
• Experience building integrations for agentic AI systems, including tool registries, function-calling logic, multi-step planning support, memory stores, and safety guardrails.
• Experience with vector databases and retrieval frameworks (Databricks Vector Search, OpenSearch, Pinecone, Milvus).
• Knowledge of LLM/GenAI concepts (prompt engineering, orchestration, multi-turn conversation flows, caching strategies, re-ranking).
• Familiarity with CI/CD practices for AI services and collaboration with MLOps (MLflow, evaluation pipelines, quality gates).
• Experience building observability into AI services—tracing, metrics, logs, alerting, and model/agent behavior monitoring.
• Strong problem-solving skills with an eye toward performance, scalability, and maintainability.
• Curiosity, adaptability, and a passion for applying emerging AI technologies responsibly and effectively.