Lead AI Engineer

Your Role At Dynatrace AI & Copilot Development

Build internal AI assistants and copilots for support, operations, and business teams.
Implement Retrieval Augmented Generation (RAG) using Snowflake data and curated metrics.
Ground AI responses using modeled enterprise data rather than documents alone.
Implement prompt strategies, guardrails, and response evaluation techniques.

Workflow & Decision Automation

Automate operational processes such as ticket triage, routing, approvals, and document handling.
Develop AI-driven classification, summarization, and recommendation services.
Implement human-in-the-loop workflows and exception handling.
Continuously improve workflows based on business outcomes and user feedback.

Production Engineering & Integration

Build and maintain AI-powered backend services, APIs, and microservices.
Integrate AI capabilities with enterprise systems (ITSM, CRM, ERP, and internal applications)
Troubleshoot failures across data pipelines, orchestration, and model inference layers.
Participate in technical design and architecture discussions.

Data Platform Integration

Utilize Snowflake as the trusted data source for AI decisions.
Use dbt models as the semantic and business logic context for automation.
Enable real-time and batch data-driven decision support.
Ensure AI actions align with defined business metrics and data definitions.

Cloud Orchestration & Observability

Implement serverless workflows using AWS (Lambda, Step Functions, API Gateway, S3, EventBridge)
Monitor system performance, latency, and operational reliability
Track AI usage, accuracy, and cost efficiency
Implement logging, auditing, and traceability of AI decisions

Minimum Requirements

• 5+ years of software or ML engineering experience, with 2+ years of building LLM systems in production.
• Expert-level proficiency in Python, plus TypeScript or Go for full-stack AI applications.
• Proven ability to communicate complex AI concepts clearly to non-technical stakeholders — translating engineering trade-offs and model behavior into business terms that inform decisions.
• Experience implementing production RAG systems using Snowflake Cortex Search, pgvector, hybrid search, and re-ranking strategies.
• Hands-on experience building MCP (Model Context Protocol) servers and clients.
• Proven track record implementing AI observability.
• Experience working with LLM APIs (OpenAI, Anthropic, Azure , Gemini) and cloud platforms (AWS SageMaker, Lambda, S3, Bedrock).
• Familiarity with CI/CD and MLOps tooling (MLflow, Weights & Biases, Snowflake ML Registry).
• Demonstrated application of responsible AI practices on live deployments, including bias checks, output validation, and human-in-loop escalation.
• Track record of proactively evaluating and introducing new AI tools or frameworks that deliver tangible improvement — not just awareness of trends but applied adoption.
• Experience managing production incidents and model rollbacks in high-stakes environments.
• Snowpark or external functions in Snowflake.
• Experience with enterprise SaaS platforms (ServiceNow, Salesforce, or similar).
• Workflow orchestration tools (Airflow, n8n, or similar).
• Authentication and access control concepts (OAuth, RBAC, SSO).
• Exposure to vector search or semantic retrieval technologies.