Principal AI Engineer
- Morningstar
- 9 - 14 years
- Mumbai
- 21 hours ago
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Job Description
Job Description Principal AI Engineer
About the Role
As a Principal AI Engineer, you will define and drive the technical direction for the systems that bring our AI products to life.
You will architect and scale the AI platform and services that enable investors worldwide to assess the Environmental, Social, and Governance (ESG) performance of companies.
Your focus will be on enterprise-grade AI engineering: LLM and ML inference platforms, orchestration, retrieval systems, resilient data pipelines, and scalable APIsbuilt with security, reliability, cost, and maintainability as first-class concerns.
Responsibilities
- Own the architecture and technical roadmap for production AI services across the product ecosystem.
- Design, build, and scale LLM and ML inference platforms, including routing, caching, model lifecycle, and API standards.
- Drive production patterns for RAG, retrieval systems, vector databases, and knowledge pipelines; set best practices for quality and relevance.
- Establish platform-level capabilities: orchestration frameworks, event-driven services, and reusable microservice components (Python-first).
- Define and enforce SLOs, reliability standards, observability practices, and incident response playbooks for AI services.
- Lead cost governance for AI (token usage, infrastructure scaling, caching, batching, evaluation-driven deployment decisions).
- Build robust CI/CD and release strategies for AI systems; automate deployments on AWS (e.g., Bedrock, Lambda, EKS, S3, etc.).
- Partner with research, engineering, security, and product leadership to align on trade-offs, delivery milestones, and risk controls.
- Provide technical mentorship and influence through design reviews, architecture forums, and by raising the engineering bar across teams.
- Evaluate new technologies and guide adoption (vector stores, orchestration tools, evaluation frameworks, MCP/server patterns) with a pragmatic lens.
Qualifications
- Expert-level programming in Python (services, APIs, pipelines, platform components).
- 9+ years of experience in AI engineering, MLOps, backend/platform engineering, or related roles with demonstrable architecture leadership.
- Proven experience deploying and operating LLMs in production at scale, including evaluation, guardrails, and cost/performance optimization.
- Strong knowledge of AWS (e.g., Bedrock, Lambda, EKS, S3), cloud-native design, and distributed systems.
- Strong experience with CI/CD, container orchestration (Kubernetes), and infrastructure automation (Terraform/CloudFormation).
- Deep understanding of microservices, event-driven architecture, queues/streams, and high-availability system design.
- Strong ML fundamentals and the ability to bridge research and engineering: model metrics, latency/throughput, inference constraints, and practical deployment.
- Experience with SQL databases (e.g., PostgreSQL) and scalable data access patterns.
- Excellent communication and stakeholder management skillsable to influence decisions across multiple teams and levels.
Nice to Have
- Experience with JavaScript/TypeScript and full-stack integration patterns for AI products.
- Experience building or operating observability platforms (CloudWatch, Prometheus, Grafana) and defining SLO-based operations.
Equal Opportunity & Work Environment
Morningstar is an equal opportunity employer.
Morningstars hybrid work environment gives you the opportunity to collaborate in person each week. Weve found that were at our best when were purposely together on a regular basis. In most locations, the hybrid work model includes four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change.
Job Classification
Industry: Financial ServicesFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Search EngineerEmployement Type: Full timeContact Details:
Company: MorningstarLocation(s): Mumbai
Keyskills: fundamentals cd continuous integration kubernetes streams system design ci/cd availability engineering microservices cloud automation stakeholder management infrastructure leadership container orchestration backend aws events queue communication skills ml architecture
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