Data Engineer (AI, GenAI & MLOps Platforms) Consultant/ Senior Consultant

Slalomis a global, human-centric business and technology consulting firm. Wespecialisein partnering withorganisationsthat aspire to excellence, helping them tackle complex challenges and achieve transformative results through strategy, technology, and business transformation services. Byprioritisingpeople, Slalom creates a unique consulting experience, with a team of strategists and engineers delivering practical, end-to-end solutions that drive impactful outcomes for our clients. We empower people andorganisationsto dream bigger, move faster, and build better tomorrows for all.

About the Role

As a Consultant/Senior Consultant in Data Engineering at Slalom, you will act as a lead data engineer, working at the forefront of modern data and AI platforms. You will partner closely with architects, data scientists, and client stakeholders to design and deliver scalable, AI-ready data platforms that power advanced analytics, machine learning, and Generative AI applications. Your engineering expertise and consulting mindset will help clients unlock business value through robust, production-grade data solutions.

What You’ll Do

• Lead the design and delivery of data platforms supporting analytics, ML, and GenAI workloads across cloud ecosystems (AWS, Azure, GCP)
• Architect and build end-to-end ML data pipelines, including ingestion, transformation, feature engineering, and model-serving data flows
• Design and implement GenAI-specific data pipelines, enabling unstructured data ingestion, embedding generation, vector indexing, and RAG architectures
• Implement and manage MLOps practices, including CI/CD for ML pipelines, automated training/inference, and pipeline orchestration
• Design and deploy model monitoring and observability frameworks, covering data/model drift, performance tracking, and auditability
• Ensure data governance, security, and quality controls are embedded into AI pipelines for compliance and reliability
• Participate in client-facing discussions, translating business needs into scalable data and AI solutions, and leading technical workstreams
• Mentor engineers, drive delivery excellence, and champion the adoption of AI-accelerated engineering practices

What You’ll Bring

• 3-5 years (Consultant)/5-8+ years (Senior Consultant) of data engineering experience with a focus on production-grade delivery and building scalable data pipelines for ML, AI, and GenAI use cases (including embeddings, vector stores, and RAG patterns)
• Strong expertise in cloud ecosystems (AWS, Azure, GCP) and hands-on experience with modern data architectures and tools such as Snowflake, Databricks, Spark, Kafka, Airflow, and dbt
• Proven programming skills in Python (required) and experience with best practices for data engineering delivery
• Strong understanding of MLOps practices, including pipeline automation, reproducibility, and ML lifecycle integration
• Experience implementing model monitoring, observability, and governance frameworks to ensure quality and compliance
• Ability to operate in client-facing environments, lead technical workstreams, mentor engineers, and drive engineering excellence

Nice to Have

• Experience with ML platforms (SageMaker, Azure ML, Vertex AI)
• Experience with vector databases (Pinecone, FAISS, OpenSearch, Weaviate)
• Exposure to LLM frameworks (LangChain, Semantic Kernel, etc.)
• Experience delivering AI solutions in regulated environments (banking, government)
• Experience with containerisation and platform engineering (Kubernetes, Terraform)