AI Engineer - Gen AI & LLM & RAG
Workload: full-time
Work model: 100% Remote
We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG).
This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role.
5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms).
Proven ability to ship production-grade LLM applications (RAG, tool/function calling, agent orchestration) with reliability, security, and observability.
Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team.
Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability.
Main Responsibilities:
Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources.
Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation.
Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs.
Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed.
Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative.
Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads.
Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation.
Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI.
Key Requirements:
Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems.
Proficiency in at least one backend language used for AI systems (Python preferred).
Hands-on experience building and integrating RESTful APIs; GraphQL experience is a plus.
Strong understanding of distributed systems fundamentals: concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization.
Experience integrating with external services and internal platforms via APIs and event-driven patterns.
Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus.
Hands-on experience building LLM-powered applications end-to-end: prompt design, tool/function interfaces, structured outputs, and streaming user experiences.
Experience with RAG systems: document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation.
Cloud services expertise: Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights).
Development practices experience: Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks).
Nice to Have:
Direct experience with LangGraph and/or LangChain for multi-agent workflows.
Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar).
Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications.
Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI.
Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control).
Other Details:
This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.
AI Engineer - Gen AI & LLM & RAG
AI Engineer - Gen AI & LLM & RAG