Software Engineer (Full Stack AI/DevOps Focus)

BWG Global•,

4d•Remote

About The Position

BWG Global is a leading provider of industry intelligence, delivering insights to over 100,000 professionals across investment firms and corporations. We specialize in identifying key market inflections and uncovering unique investable ideas through proprietary research products built on decades of experience. Our mission is to aggregate, analyze, and compare information to identify dislocations or inflection points. We have a global presence spanning North America, Europe, and Asia, and our products include Channel Research Reports, Market Insight Surveys, and live Forum events. We are seeking an experienced Senior Software Engineer to join our technology team, reporting directly to the CTO. This role is a key technical contributor across our full-stack environment, with a primary focus on modernizing our DevOps practices, building AI-powered capabilities, and writing high-quality production code within a small, collaborative team. This is a unique opportunity to shape the engineering culture at one of the most differentiated investment research firms in the industry, with exposure to end-to-end business processes and close collaboration with colleagues at all levels.

Requirements

5–8 years of professional software engineering experience in a full-stack or back-end role
Strong Python skills — back-end services, scripting, API development, and automation
Lightweight Python web frameworks (Flask, FastAPI, or similar) — building internal tools, glue services, and UI prototypes end-end
Azure DevOps — source control, CI/CD pipelines, pull request workflows, branching strategies, and release management
REST API fluency — OpenAPI/Swagger spec design, OAuth 2.0 / Entra ID auth flows, versioning, pagination, and rate limiting patterns
AI development: MCP (Model Context Protocol) gateway implementation, RAG architectures, vector search and embeddings, and LLM API integration (OpenAI, Anthropic, or equivalent)
LLM observability — logging token usage, latency, and output quality in production AI systems
Async and event-driven patterns — webhooks, streaming APIs, message queues (Azure Service Bus or equivalent)
Production-grade code quality — instrumentation, structured logging, monitoring, and alerting
Collaborative engineering practices — code review, documentation, and working effectively in small agile teams

Nice To Haves

TypeScript / JavaScript — front-end interface work and React component development
SQL and Snowflake — querying and working alongside data pipelines and warehouse integrations
Prompt engineering and system prompt design for client-facing AI products
Salesforce development or integration experience (Apex, Salesforce APIs, SSE/Streaming API)
Snowflake DBT data pipeline and Streamlit app building and support
Azure ecosystem depth — Azure Functions, Azure AI Search, App Services
OutSystems (O11) development or integration experience
Experience in regulated or compliance-conscious environments
Computer Science degree or equivalent technical education

Responsibilities

Lead the migration to a robust and documented source control and pull request workflow using Azure DevOps.
Establish CI/CD pipelines, branching strategies, and code review practices.
Implement and maintain our MCP (Model Context Protocol) gateway and related AI tooling, integrating with Azure AI Search, OpenAI/Anthropic APIs, and RAG pipelines.
Develop and maintain features across our client-facing research portal and internal tooling.
Work across Python back-end services, REST APIs, and front-end interfaces.
Take ownership of code quality—instrumentation, logging, observability, and performance.
Participate in code review and help establish standards for the team.
Work across a complex integration landscape including Azure services, Salesforce, Snowflake, and OutSystems-hosted APIs.
Work alongside other full-stack, data, and ML engineers.
Share knowledge, review peers’ code, and contribute to a culture of quality and continuous improvement.
Manage your own delivery cadence with limited oversight.
Estimate, prioritize, and communicate progress clearly in an agile environment.