Designs and deploys production AI assistants, copilots, agents, RAG systems, and intelligent automations. Integrates LLMs with Snowflake, AWS, enterprise applications, and workflow platforms to automate business processes. Builds backend services and serverless workflows, implements observability and responsible AI controls, and scales validated prototypes into reliable production solutions. Partners with technical and business teams to improve decision-making, efficiency, quality, and cost performance.
Your role at Dynatrace
As part of a new team focused on accelerating AI adoption, the Sr AI Engineer will design and deploy AI-powered automation and intelligent agents embedded directly into business operations. This role takes AI capabilities proven in business-function POCs and scales them into governed, production-grade solutions, using Snowflake as the enterprise data foundation and a combination of cloud services and intelligent automation platforms for orchestration and execution.
The position partners with Data, IT, and business teams to automate workflows, support decision-making, and improve operational efficiency. The focus is on applying AI to real business processes, not research or model training.
Key Responsibilities
AI & Copilot Development
- Build internal AI assistants, copilots, and agents for support, operations, and business teams
- Implement Retrieval Augmented Generation (RAG) using Snowflake data and curated metrics
- Ground AI responses in modeled enterprise data rather than documents alone
- Implement prompt strategies, guardrails, and response evaluation techniques
Workflow & Intelligent Automation
- Automate operational processes such as ticket triage, routing, approvals, and document handling
- Combine LLMs and AI agents with intelligent automation platforms (Workato, Power Automate, UiPath) to automate business processes end to end
- 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
- Scale proven AI POCs into production-ready, supportable solutions
- 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 reliability
- Track AI usage, accuracy, and cost efficiency
- Implement logging, auditing, and traceability of AI decisions
- 5+ years of software or ML engineering experience, including 2+ years building LLM-based systems in production
- Expert-level proficiency in Python, plus TypeScript or Go for full-stack AI applications
- Hands-on experience building production RAG systems, including vector or hybrid search and re-ranking strategies
- Experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Gemini) and AWS services (Lambda, S3, Bedrock, SageMaker)
- Proven track record implementing AI observability, including tracking quality, latency, usage, and cost
- 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-the-loop escalation
- Experience managing production incidents and model rollbacks
- Ability to explain AI concepts and engineering trade-offs clearly to non-technical stakeholders in business terms
- A track record of evaluating and adopting new AI tools or frameworks that deliver measurable improvement
Preferred Qualifications
- Hands-on experience building MCP (Model Context Protocol) servers and clients
- Snowflake Cortex Search, Snowpark, or external functions in Snowflake
- Experience with intelligent automation and workflow platforms (Workato, Power Automate, UiPath, n8n) and orchestration tools (Airflow), including integrating them with LLMs and AI agents
- Experience with enterprise SaaS platforms (ServiceNow, Salesforce, or similar)
- Authentication and access control concepts (OAuth, RBAC, SSO)
- Dynatrace is a leader in unified observability and security.
- We provide a culture of excellence with competitive compensation packages designed to recognize and reward performance.
- Our employees work with the largest cloud providers, including AWS, Microsoft, and Google Cloud, and other leading partners worldwide to create strategic alliances.
- You'll get to work at the forefront of innovation with Dynatrace Intelligence—the industry's first agentic operations system. Bringing together deterministic and agentic AI, it helps teams understand what's happening, why it matters, and what to do next— automatically.
- Over 50% of the Fortune 100 companies are current customers of Dynatrace.
Dynatrace Mumbai, Maharashtra, IND Office
Mumbai, India
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