Strong fit for engineers who design and operate search systems with AI integration, who have hands-on experience with Elasticsearch and Python services in a hybrid work arrangement.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Guidepoint via Greenhouse
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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This job posting is for an existing vacancy. The salary offered will be based on the successful candidate’s skills, experience, qualifications, and location.
Compensation$135,000—$155,000 CAD
Source: Guidepoint careers (Greenhouse)