Senior Software Engineer @ Microsoft, Azure AI Search — building the vector search, agentic retrieval, and RAG systems behind enterprise AI.

Hi, my name is Robert! I'm a Senior Software Engineer on Microsoft's Azure AI Search team, where I build the retrieval engine behind enterprise AI. My work sits at the intersection of information retrieval, vector search, and applied machine learning: improving relevance for complex global queries, grounding LLMs and multi-agent workflows in governed enterprise knowledge, and turning research prototypes into production-grade agentic retrieval and RAG capabilities.

Earlier, as a Software Engineer II, I delivered the core of our vector engine — including quantization techniques that cut customer costs by 8–32× and latency by up to 20×, a hybrid-search relevance stack, and an extensible facet-aggregation engine built on formal grammar and parsing. I care deeply about performance, correctness, and distributed systems, and I'm often the engineer who root-causes the gnarliest production incidents.

Beyond shipping code, I love building communities. I graduated with a B.Eng in Electrical & Computer Engineering (97% CGPA, 20+ awards worth over $100K), drove a 200+ person conference from vision to reality, and grew a Senior's Program to 180+ volunteers and 650+ participants while delivering technical talks to 250+ engineering students. I also led a student team to train a deep neural network for human pose estimation from randomly initialized weights — check it out on Streamlit.

Please reach out if you'd like to connect!

Resumé

Senior Software Engineer, Microsoft Azure AI Search

Mar. 2025 - Present | Redmond, WA

C++, C#, Java, Python | Vector Search, Agentic Retrieval, Information Retrieval, RAG, Distributed Systems

- Driving a novel vector-search diversity capability end-to-end: with no existing blueprint, owning scoping, surfacing engine-level constraints, and proposing and refining the design - aimed at improving relevance for complex global queries through diversity-aware sampling that broadens retrieved evidence for higher-quality answer synthesis across enterprise indexes holding billions of vectors.
- Led a cross-team effort to bridge research and production, defining feature scope, data-driven ship criteria, and validation methodology to move multiple agentic-retrieval quality improvements from prototype to shipped Public Preview features.
- Integrated Azure AI Search retrieval into agent workflows - grounding LLM agents in governed, indexed enterprise knowledge across tool calling, multi-agent orchestration, and Retrieval Augmented Generation.
- Architected a benchmarking system from scratch to profile highly variable agentic-retrieval workloads across CPU, memory, and throughput - modeling latency distributions, tool-calling iterations, and dependency patterns to produce the production billing model that enabled a new serverless enterprise search offering to launch; surfaced and resolved memory optimizations along the way.
- Serve as a technical leader at the intersection of information retrieval, vector search, Azure OpenAI, and agent orchestration - mentoring and onboarding engineers, leading design reviews, and driving deeply technical architecture decisions.

Software Engineer II, Microsoft Azure AI Search

Jun. 2022 - Feb. 2025 | Redmond, WA

C++, C#, Java, Python | Vector Search, Vector Quantization, Information Retrieval, RAG, Distributed Systems

Drove vector storage, quantization, and relevance for Azure AI Search's vector retrieval engine, spanning billions of embeddings.

- Drove vector quantization from Public Preview through to GA - now widely adopted by customers - delivering 8-32x cost savings and up to 20x latency reduction through binary vectors, scalar and binary quantization, and SIMD-accelerated distance computation.
- Strengthened the relevance stack by designing hybrid-search subscore fusion and score thresholding to improve result quality across blended vector and keyword retrieval.
- Designed and shipped a data-driven quota-enforcement mechanism for HNSW indexes tied to physical resource utilization, cutting limit overshoot by 100x through cross-team design collaboration.
- Owned end-to-end delivery of an extensible facet-aggregation engine from spec, building a custom lexer-parser-evaluator that applies BNF grammar, the shunting-yard algorithm, and Reverse Polish Notation to parse, simplify, and validate expressions - backed by extensive A/B test coverage.
- Hardened the vector search engine with a new test suite that caught a critical bug in a new quantization algorithm before release.
- Acted as a subject-matter expert and key reviewer - root-causing deeply technical production incidents across teams to rapidly restore customer service and drive durable fixes, while raising reliability through rigorous review of distributed-systems and vector-algorithm changes.

Software Engineer, Microsoft Azure AI Search

Jul. 2021 - May 2022 | Redmond, WA

- Delivered the new index alias feature to Public Preview, letting customers repoint applications to a different underlying index with no code changes or downtime.
- Modernized a core telemetry-database table through a phased, zero-downtime migration that preserved data integrity and delivered a 50-100x query speedup.