AI Engineer

Job Summary

We're seeking an experienced AI Engineer to help shape the future of enterprise automation. As part of our mission to redefine integration, you'll infuse AI into our cutting-edge iPaaS platform, building intelligent, self-service systems that enable businesses to accelerate partner onboarding, automate B2B data exchanges, and gain agility through smart, context-aware features.

Key Responsibilities

• Design and develop AI-powered features to assist customers in configuring integrations, transforming data, and resolving errors using natural language.
• Architect and deploy Retrieval-Augmented Generation (RAG) pipelines combining LLMs with real-time enterprise data.
• Integrate Azure OpenAI services and fine-tune prompt engineering strategies to ensure scalable, production-grade solutions.
• Manage vector databases (Milvus, FAISS, Pinecone, Azure Cognitive Search) to enable semantic search and contextual data retrieval.
• Build AI agents using LangChain or Semantic Kernel to automate multi-step integration workflows.
• Collaborate with Product and UX to deliver user-centric AI features that simplify complex technical tasks.
• Implement containerized, scalable deployments using Docker, Kubernetes, and CI/CD pipelines.

Required Skills

• 58 years of experience in AI/ML engineering, especially in production systems.
• Deep expertise with LLMs, prompt engineering, and AI feature design.
• Proven success building RAG pipelines and implementing effective vector search.
• Proficiency with vector databases and embedding APIs.
• Hands-on experience with Azure OpenAI and GPT models.
• Strong Python development skills and familiarity with Hugging Face Transformers.
• Solid foundation in NLP, unstructured data processing, or knowledge graph development.
• Practical experience with Docker, Kubernetes, and CI/CD for robust AI system deployments.

Nice to Have

• Familiarity with TensorFlow, PyTorch, or similar ML frameworks.
• Experience fine-tuning LLMs on custom or domain-specific datasets.
• Exposure to MLOps tools and workflows.
• Background in B2B integrations, data mapping, or enterprise automation domains.