Senior Software Engineer - Search & AI

Overview: 

We are hiring a Senior Software Engineer to build and run the search systems that connect Guidepoint clients with the right experts. You will join the Search team in Data Engineering and own work end to end: Elasticsearch index design and cluster operations, the inference services behind semantic search, the Python services in front of it all, and the AI features that turn a plain-language question into a ranked list of people.

Our search sits on the critical path of the business. When a client asks for experts, the quality and speed of what comes back decides whether a project starts. This role is for someone who cares about relevance as much as uptime, and who can explain why a result ranked where it did.

This role will be hybrid based in our Toronto office. 

What You'll Do:

Search and indexing

• Design index mappings, analyzers, and tokenizers for expert, company, project, and lead data, including semantic_text and dense_vector fields
• Write and tune queries: bool, function_score, nested, aggregations, kNN, and hybrid retrieval
• Plan and run reindexes, Painless data fixes, and alias cutovers with zero downtime (we recently retired Solr completely)
• Build and maintain ingestion: Logstash JDBC pipelines and incremental change feeds from PostgreSQL and SQL Server, including tuning the source SQL when indexing slows down

Cluster operations and maintenance

• Operate Elastic Cloud deployments on Azure across prod, QA, and dev: sizing, shard allocation, ILM, snapshots, and version upgrades
• Monitor cluster health in Datadog and Kibana; tune heap, circuit breakers, and fetch sizes; diagnose shard and recovery problems with the _cat and _cluster APIs
• Manage access: roles, service API keys and their rotation, least-privilege accounts for every consumer
• Lead incident response for latency spikes, circuit breaker trips, and ingestion failures, then write the RCA and ship the follow-up fixes
• Load test search under realistic traffic before major mapping, query, or capacity changes

Inference and AI services

• Run the inference layer behind semantic search: Elasticsearch inference endpoints, embedding models (ELSER, E5, or hosted models), ML node capacity, and cost
• Build services that turn natural-language requests into structured search queries and filters using LLMs
• Build and harden MCP servers that let AI agents call search as a tool: auth, consistent error contracts, lean payloads
• Benchmark LLMs for accuracy, latency, and cost against fixed query batteries, and pick models on evidence
• Set up offline relevance evaluation (judgment lists, nDCG, recall@k) and run A/B tests online

Python services and APIs

• Build and maintain the Python APIs and workers, running on AKS (Kubernetes), that sit between clients, AI agents, and the search cluster
• Own services in production on AKS behind Azure APIM: tests, CI/CD, Docker images, Kubernetes deployments and scaling, dashboards, and alerts
• Fix findings from security scans and keep secrets out of code and images
• Review code and raise the bar on design, testing, and readability across the team

Across the team

• Turn ambiguous product asks into technical designs and break them into deliverable pieces
• Write design docs and runbooks that others can pick up without you in the room
• Mentor engineers on search and backend practices

What You Have:

• 6+ years of professional software engineering, much of it building and running backend systems in production
• Bachelor’s Degree in Computer Science, Engineering, Information Technology or related field
• Search engines, in depth (3+ years hands-on with Elasticsearch, Solr, or OpenSearch): mappings and analyzers, Query DSL, relevance tuning, aggregations, cluster sizing and operations, reindexing strategies. You've owned a cluster, not just queried one.
• Python: strong, idiomatic Python 3 (typing, async, packaging, pytest) and at least one web framework such as Flask or FastAPI
• Software fundamentals: data structures and algorithms, API design, concurrency, caching, and SQL. You test your code, profile before you optimize, and can reason about failure modes in distributed systems.
• AI in practice: you've shipped at least one feature that uses embeddings or LLMs, and you know the tradeoffs between lexical, semantic, and hybrid retrieval, including what it takes to run embedding inference in production
• Operational ownership: comfortable with Docker, Kubernetes (ideally AKS), CI/CD, and observability tooling; you've been on call, run a search cluster through upgrades and incidents, and written the RCA afterward
• Communication: clear written design docs and the ability to explain search behavior to non-engineers

Nice to Have:

• Elastic certification (Certified Engineer) or deep experience with Elastic 8.x vector search, ELSER, or ES|QL
• Migrated a production workload from Solr or OpenSearch to Elasticsearch
• Learning-to-rank, cross-encoder rerankers, or relevance feedback loops
• Built RAG pipelines or LLM agents, including evaluation and guardrails
• Logstash, Kafka, or RabbitMQ for indexing pipelines; load testing with JMeter or k6
• Azure (AKS, ACR, Pipelines) and Datadog
• Some PHP or TypeScript, since a few of our consumers are written in them

What We Offer: 

The annual base salary range for this position is 135,000-155,000. Additionally, this position is eligible for an annual discretionary bonus based on performance.

You will also be eligible for the following benefits:

• Paid Time Off
• Comprehensive benefits plan
• Company RRSP Match
• Development opportunities through the LinkedIn Learning platform

About Guidepoint: 

Guidepoint powers end-to-end research workflows for the world’s best research teams.

Backed by a global network of more than 2 million experts and over 1,600 employees, Guidepoint delivers real-time access to expertise, primary research, and actionable knowledge that help organizations make informed decisions. Through consultations, surveys, events, proprietary content, and AI-enabled tools, we support every stage of the decision-making process—from developing hypotheses and gathering insights to validating assumptions and building conviction for critical decisions.

At Guidepoint, our success relies on the diversity of our employees, experts, and clients, which enables us to foster meaningful connections and a broad range of perspectives. We are committed to creating an inclusive and welcoming environment where individuals of all backgrounds, identities, and experiences can contribute and succeed.

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