Principal AI Engineer at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

0.0

Posted On

22 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Machine Learning, Large Language Models, Generative AI, Prototyping, Cloud Services, Security, Stakeholder Management, Technical Leadership, Evaluation Metrics, Data Contracts, Programming, Cross-Functional Initiatives, Incident Readiness, Mentoring, Customer Engagement

Industry

Software Development

Description

- Lead zero-to-one (0→1) incubation R&D through MVP and private preview, then drive one-to-many (1→N) platformization and scale to GA; make principled tradeoffs across quality, latency, reliability, cost, and safety. - Provide hands-on technical leadership: prototype in code, review designs and Pull Requests (PRs), define Application Programming Interfaces (APIs)/data contracts, build comprehensive well-architected systems, and establish evaluation frameworks to de-risk complex systems. - Set strategy for AI-native security experiences and platform components: where to use Large Language Models (LLMs) versus classical Machine Learning (ML), retrieval/Retrieval-Augmented Generation (RAG) design, grounding, model routing/fallbacks, and safety guardrails to meet customer outcomes and Service Level Objectives (SLOs). - Ensure Responsible AI, privacy, and security guardrails are designed in from day one, coordinate safety reviews, abuse prevention, compliance, and incident readiness. - Lead v-teams and mentor others; cultivate a builder culture of velocity and quality as a force multiplier. Engage directly with enterprise customers and field to co-design solutions and land adoption; communicate program status and strategy to executives with hands on real code demonstrations. - Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. - 6+ years of experience driving complex, cross-functional initiatives; experience leading without authority across multiple teams. - 3+ years working with Machine Learning (ML)/Artificial Intelligence (AI) systems (e.g., Large Language Models (LLMs)/Generative AI (GenAI), retrieval/Retrieval-Augmented Generation (RAG), model serving, experimentation platforms, data pipelines) including establishing evaluation metrics and improving model quality. - Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. - Experienced in program leadership, communication, and stakeholder management skills with the ability to influence leaders and make data-informed decisions. - Proven track record shipping cloud services or platforms at scale (multi-tenant, high-throughput) with measurable customer and business impact. - Security domain expertise (e.g., threat detection/response, SIEM/SOAR, identity, endpoint, cloud security) and familiarity with analyst workflows. - Experience with GenAI/LLM techniques and tooling (prompt engineering, retrieval/vector stores, agents/tool use, content safety/guardrails, offline/online eval frameworks, vibe coding). - Hands-on coding ability in one or more languages (e.g., Python, C#, C++, Rust, JavaScript/TypeScript); comfortable prototyping, reading Pull Requests (PRs), and engaging deeply in technical design reviews. - Demonstrated success driving zero-to-one (0→1) initiatives from ambiguity to Minimum Viable Product (MVP) to General Availability (GA) and then to one-to-many (1→N) platform adoption across multiple product teams. What It Takes to Thrive as a Principal AI Engineer Here: - Model-literate and pragmatic: you know when to use a Large Language Model (LLM), when a deterministic/rules-based or classical Machine Learning (ML) approach is better, and how to hybridize them with retrieval, caching, routing, and fallbacks to meet Service Level Objectives (SLOs) and cost targets. - Evaluation-obsessed: you can define the right metrics and datasets (clarity, groundedness, precision/recall, latency/cost etc.), build the eval harness, and insist on measurable improvements before broad rollout. You define durable data/Application Programming Interface (API) contracts and Software Development Kits (SDKs), negotiate dependency graphs across partner teams to meet delivery dates and Service Level Objectives (SLOs) within complex cost, security/privacy/compliance constraints to ensure operational readiness. - Customer-back and outcome-focused: you spend time with defenders and admins, translate workflows into crisp specs, land adoption, and iterate quickly based on feedback and telemetry. - Clear communicator and connector: you create clarity in ambiguity, align diverse stakeholders across research/engineering/design/field, and mentor others to raise the bar.

Responsibilities

Lead R&D initiatives from inception to scale, ensuring quality and safety. Provide technical leadership and mentor teams while engaging with customers to co-design solutions.