Senior AI Engineer

The Job in short

• At
  Backbase
  , we're redefining how banks deliver customer experiences by embedding intelligence into every digital interaction. Our Engagement Banking Platform puts the customer first — and behind the scenes, our AI team powers the intelligence that makes it personal, predictive, and responsive.
• As a
  Senior AI Engineer
  – Consultant, you'll be part of our Consulting AI team, working hands-on to deliver real-world machine learning and generative AI systems in production. You'll collaborate with our Principal AI Lead Consultant, data engineers, and client delivery teams to build, tune, and scale AI solutions across onboarding, fraud, servicing, and document intelligence.
• This is a role for engineers who don't just prototype — they ship. If you're excited about LLMs, RAG pipelines, and turning foundation models into enterprise-grade systems, you'll thrive here.

Meet the job

You will be part of our Consulting AI team and act as a senior technical contributor to AI engagements. You'll work directly with innovative banks to help shape and deliver high-impact AI initiatives using both Backbase and non-Backbase technologies.

This is a hybrid role combining solution design, engineering implementation, and delivery execution. You'll develop AI systems — from classical ML to modern GenAI solutions — while working alongside a high-performing, multidisciplinary team.

Responsibilities:

● Design and implement AI solutions across a range of banking use cases: onboarding intelligence, fraud detection, conversational banking, smart servicing, and document intelligence

● Build secure, scalable systems leveraging LLMs, embedding models, RAG pipelines, and agentic workflows

● Support client-facing engagements by contributing to solution designs that integrate with both Backbase and heterogeneous technology stacks

● Collaborate with product, engineering, and consulting teams to deliver enterprise-grade AI systems

● Building secure and scalable LLM-based systems using LangChain, LangGraph, and Databricks

● Support MLOps/LLMOps practices, including CI/CD for models, prompt versioning, monitoring (e.g., Langfuse), and retraining workflows

● Work as part of a multidisciplinary team of AI/ML Engineers and Data Engineers with a focus on delivery quality and performance

● Stay up to date with trends like small/tuned domain-specific models, agent-based orchestration, LLM observability frameworks (e.g., Langfuse), and responsible AI governance

● Contribute to client workshops, technical documentation, and internal capability development

How about you

● You've deployed AI/ML models in production — and understand the nuances of performance, drift, and observability

● You have hands-on experience integrating models into APIs, microservices, or enterprise workflows

● You know how to manage the lifecycle of LLMs — from prompt design and embedding generation to context tuning and monitoring

● You're fluent in Python and comfortable working with AI/ML libraries (e.g., HuggingFace Transformers, PyTorch, TensorFlow)

● You've worked with LangChain, LangGraph, Databricks notebooks, and Spark pipelines

● You're curious about GenAI trends but also pragmatic: you know what belongs in production and what doesn't

● You're familiar with the principles of responsible AI, including fairness, explainability, and compliance in regulated industries

● You collaborate well with cross-functional teams, and have experience working in consulting or enterprise delivery environments

Requirements

● 6+ years in AI/ML or applied data science, with 2–3+ years building production-grade systems

● Strong proficiency in Python and frameworks such as PyTorch, TensorFlow, and HuggingFace

● Practical experience with LLM applications: prompt engineering, embedding pipelines, RAG workflows

● Familiarity with vector databases (e.g., FAISS, Weaviate, Chroma, Milvus)

● Hands-on with LangChain, LangGraph, and Langfuse for agent orchestration and observability

● Experience working in Databricks, leveraging Delta Lake and Spark for data processing

● Exposure to MLOps/LLMOps workflows: prompt versioning, CI/CD pipelines, trace logging, and automated evaluations

● Demonstrated success supporting technical delivery in consulting, integration, or platform engineering teams

● Strong documentation habits and clear communication skills for working in distributed, cross-functional teams