About the role

AI Engineer developing AI solutions and pipelines within a hybrid team at Solirius Reply. Collaborating with Data Scientists and Engineers to tackle real-world problems using cutting-edge technology.

Responsibilities

Design, build, and maintain scalable AI architectures and pipelines that support business requirements, ranging from predictive modelling to Generative AI applications.
Operationalise Large Language Models (e.g., GPT, Llama, Mistral) and NLP workflows (BERT, topic modelling) for production environments.
Design and orchestrate autonomous agentic workflows (using frameworks like LangChain, AutoGen, or CrewAI) that can reason, plan, and execute tasks across multiple tools.
Work collaboratively with Data Engineers to ensure model deployments are supported by robust data storage, vector databases, and cloud infrastructure.
Act as a practitioner and mentor for AI-assisted coding. Champion the use of tools (e.g., Claude Code, Cursor, Copilot) and help upskill internal teams and clients on agentic coding workflows.
Provide technical consulting on AI product development, helping clients understand the distinction between standard ML and agentic AI.
Stay updated with the latest trends in the AI engineering field, such as the modern AI stack, synthetic data generation, and Explainable AI.

Strong proficiency in Python and robust software engineering principles, with experience operating as part of engineering teams.
Proven experience working with LLMs and designing Retrieval-Augmented Generation (RAG) architectures, including knowledge of semantic search and vector retrieval.
Experience using AI-assisted and agentic coding tools to accelerate development, with the ability to articulate and teach these methods to others.
Passionate, motivated, and enthusiastic about developing technology solutions with the ability to explain complex AI concepts to non-technical stakeholders.
Experience designing and orchestrating autonomous agentic workflows (e.g., LangGraph) and managing agent state/memory (Bonus).
Experience working with cloud infrastructure (Azure, AWS, or GCP) (Bonus).
Experience working in an Agile development environment (Bonus).
Proficiency in SQL and relational databases (e.g., PostgreSQL, DuckDB). Experience with the modern data stack, building data ingestion pipelines and working with ETL and orchestration tools (e.g., Airflow, Luigi, Argo, dbt), big data technologies (Spark, Kafka, Parquet), and web frameworks for model serving (e.g. Flask or FastAPI) (Bonus).
Familiarity or experience with classical NLP techniques (BERT, topic modelling, summarisation), statistical analysis, and knowledge graphs (Bonus).
Familiarity or experience with Deep Learning (TensorFlow, PyTorch) (Bonus).