Role Description

As a Software Engineer, Artificial Intelligence, you will play a key role in building, operating, and continuously improving AI-driven systems that support internal business capabilities. You will focus on designing and maintaining AI-powered data pipelines, agent-based solutions, and LLM-driven services, working closely with modern data and ML platforms.

This role requires strong software engineering foundations combined with hands-on experience in large language models, vector stores, and ML lifecycle management. You will contribute to scalable, production-grade AI solutions while driving quality, reliability, and continuous improvement across the full model and data pipeline lifecycle.

• Build and maintain AI-powered data pipelines and extraction processes (batch and streaming) from internal relational data sources and unstructured documents (PDF, Word, PowerPoint) into structured datasets within Databricks.
• Own end-to-end delivery of AI solutions, from design through production.
• Design and manage text embeddings and vector stores within Databricks for use with vector indexing and retrieval solutions.
• Drive architecture and best practices for AI-powered systems.
• Design, develop, and maintain custom tools implemented as MCP servers and Databricks applications to extend agent and model capabilities.
• Design, develop and implement AI Agents using frameworks like LangChain and LangGraph.
• Implement LLM scorers to validate and monitor agents, applications and models. Prevent issues like hallucinations or unnecessary actions through structured testing and guardrails.
• Drive continuous improvement through prompt engineering, pipeline optimization, vector store tuning, and scorer refinement to ensure high-quality LLM responses.
• Collaborate on production deployments, monitoring, and scalability of ML and LLM-based services.

Qualifications

• 5-8 years of industry experience in software engineering or related roles.
• Strong proficiency in Python, including production services, asynchronous programming, and testing.
• Hands-on experience with AI Agents Development frameworks such as LangChain, LangGraph and LlamaIndex.
• Experience using MLflow for prompt engineering, experimentation, evaluation, model registry, and deployments.
• Solid understanding of vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma or similar), including serverless or managed options.
• Experience implementing Retrieval augmented generation (RAG) solutions. Data Ingestion and Retrieval, LLM Generation.
• Experience building and consuming REST APIs, model serving solutions, and CI/CD pipelines.
• Experience working with cloud platforms (AWS), containerization (Docker), and modern deployment practices.
• Hands-on experience with Databricks, Apache Spark, and Delta Lake (nice to have).
• Advanced English level, both written and verbal.