Data Engineering

Data Engineering roles: Strong hands-on Python engineering skills, including production-grade code, modular design, testing, packaging, and modern tooling (uv/poetry, pydantic, FastAPI, async patterns) Practical AI engineering experience - building, integrating, and deploying LLM-powered applications including RAG pipelines, agentic workflows, tool/function calling, prompt engineering, evaluation harnesses, and guardrails Working knowledge of major LLM providers and frameworks (Claude, OpenAI, Gemini, open-source models via Bedrock/Vertex/Azure AI, LangChain/LangGraph, LlamaIndex, Haystack) and the trade-offs between them Graph database expertise - modelling, querying, and operating graph stores such as Neo4j, Amazon Neptune, or TigerGraph, including Cypher/Gremlin, ontology design, and integrating graphs with vector stores for GraphRAG patterns Comfortable across the broader data stack: ingestion and ELT (Airflow, dbt, Fivetran), cloud data platforms (Snowflake, Databricks, BigQuery), vector databases (Pinecone, Weaviate, pgvector), and storage formats (Parquet, Iceberg, Delta) Builds robust, observable pipelines with appropriate logging, monitoring, lineage, cost controls, and CI/CD; treats AI components as production software, not notebooks Cloud-native delivery on at least one of AWS, Azure, or GCP — including IaC (Terraform), containerisation (Docker), and serverless or Kubernetes-based deployment of AI workloads Consulting skills equivalent to a Big 4 environment: structured problem-solving, clear written and verbal communication, stakeholder management, and the ability to lead workshops and present designs to technical and business audiences Translates ambiguous client requirements into concrete technical designs, including data models, integration patterns, solution architectures, and effort estimates Works directly with client engineering teams and architects, providing uplift on AI engineering best practice, code reviews, and standards Collaborates closely with the Leads and offshore delivery teams, owning technical quality of deliverables and ensuring alignment between design intent and implementation Stays current on the rapidly evolving AI and data engineering landscape, with a pragmatic view on what is production-ready versus experimental