Senior AI Engineer

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msg global solutions is a systems integrator, software development partner, and managed services provider focused on SAP solutions for multiple industries. Our services include strategies for accounting, finance, regulatory reporting, performance management, sustainability, customer experience, and IoT. Operating from offices across the globe and growing, we help clients achieve operational efficiency and improve decision-making capabilities. With deep industry knowledge, technical expertise, and a diverse range of perspectives, our people spark change and create innovative solutions to complex operational issues. Our goal is to create long-lasting client relationships built on trust and dependability.

msg global solutions is part of msg, an independent, international group of companies with more than 10,000 employees around the world.

msg global solutions leverages cutting-edge technologies, including AI, to solve business challenges. We partner with industry leaders like SAP and AWS to deliver enterprise solutions to Fortune 500 companies, transforming data into valuable insights.

Our solutions reshape industries such as finance, insurance, and manufacturing, specializing in:

• Advanced analytics and AI technologies
• Industry-specific technologies and IoT integration
• Data-driven operational excellence

By partnering with us, organizations can transform their business models, uncover new revenue streams, and achieve operational excellence. As part of the msg group, we combine specialized agility with global IT resources to shape the future of business.

You build the things that actually do the work. Starting from a business process and the people who run it, you decide how that process breaks down into steps a model can handle, what context each step needs, which tools it may call, when a human has to be asked, and how anyone can tell whether the result is correct.

This is applied engineering rather than research. You will not be training foundation models. You will be making general-purpose ones reliable enough that a business is willing to let them touch real work, which is a harder problem than it sounds and a different skill entirely.

Accuracy is the number you own. When something works 80% of the time and has to work 98% of the time, closing that gap is the job.

• Turn business processes into working agents: decompose the process, define the steps, the tools, the handoffs and the points where a human has to decide.
• Design and build retrieval pipelines: chunking and indexing strategy, embedding model selection, hybrid dense and keyword search, cross-encoder reranking, and grounding that keeps answers tied to real source data.
• Treat prompt and context engineering as an engineering discipline, with versioning, tests and measurement rather than tweaking by feel.
• Build the evaluation layer: golden datasets defined together with business experts, automated scoring, model-graded evals where they are appropriate, and regression suites that run on every change.
• Instrument production systems so quality is observed rather than assumed: traces, token cost, latency, failure rates, and drift as data and models change underneath you.
• Own quality end to end. Diagnose whether a failure came from retrieval, reasoning, tool design or the underlying data, and fix the actual cause.
• Choose models and manage the trade-off between quality, cost and latency, and revisit those choices as the available models change.
• Work directly with business experts to extract the domain logic that exists only in their heads and turn it into something testable.
• Take agents into production with the platform and infrastructure team, and stay accountable for how they behave once they are live.

Requirements

• 5+ years building software, including 2+ years on LLM-based systems that reached production and real users.
• Strong Python and the habits of a working engineer: version control, tests, code review, incremental delivery. Comfortable with async work, FastAPI and Pydantic or their equivalents.
• Hands-on with at least one agent orchestration framework (LangGraph, LlamaIndex Workflows, OpenAI Agents SDK, Temporal or comparable) and a clear view of what belongs in the framework and what belongs in your own code.
• Experience serving models through a managed platform (AWS Bedrock, Azure AI Foundry, Google Vertex AI) or directly against provider APIs, including streaming, structured output, prompt caching and rate-limit handling.
• Production vector search with at least one of pgvector, Qdrant, Pinecone, Weaviate, Milvus or Azure AI Search, and an opinion on the trade-offs between them.
• Deep practical