Data & AI Reliability Engineering Consultant/Architect

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Data Technology Consulting

We are hiring Data & AI Reliability Engineering Consultants and Architects to lead client-facing discovery, presales and advisory work on the reliability of data platforms and AI systems. The role is a consultant: the person owns the conversation with the client, shapes the solution and the proposal, and then guides the engineering team that builds it.

The consultant covers three connected areas: observability and SRE practice, data platform reliability (pipelines, quality, lineage, cost), and AI/LLM system reliability (evaluation, telemetry, guardrails).

Чим ви будете займатися у цій ролі

Lead the technical side of presales: qualify the request, run client workshops, define scope, assumptions and estimates
Run discovery and maturity assessments of a client's observability, data reliability and AI operations; deliver findings and a prioritised roadmap
Write the solution part of proposals and RFP responses; present and defend it to client technical and business stakeholders
Design target architectures for observability and reliability of data platforms and AI/LLM workloads, including tool selection and migration paths
Define SLOs, SLIs and error budgets for data products and AI services; translate them into alerting, incident and governance processes
Build the business case: cost of incidents, tooling cost optimisation, expected effect of the change
Act as the trusted advisor for client engineering leads, SDMs and directors during the engagement
Lead the first phase of delivery after a won deal, then hand over to the engineering team while staying accountable for the solution
Review the work of engineers, set technical standards (alert-as-code, dashboards-as-code, IaC), unblock decisions
Turn project experience into reusable assets: offerings, accelerators, assessment frameworks, reference architectures
Mentor engineers growing towards consulting; take part in technical interviews
Represent Data & AI externally: talks, articles, vendor partnerships

Навички

7+ years in engineering, of which 2+ years in a client-facing role: consultant, solution architect, presales engineer or technical lead with direct client ownership
Proven presales record: led discovery or assessment workshops, produced estimates and proposals, presented to senior stakeholders
Able to structure an ambiguous client problem into scope, options, trade-offs and a recommendation, in writing and live
English B2+ with confident spoken delivery; can run a workshop and handle objections without support
Hands-on background with at least one enterprise observability platform: New Relic, Datadog, Splunk, Dynatrace, Grafana stack or Elastic
OpenTelemetry, distributed tracing, metrics and log pipelines; alert design, event correlation and noise reduction
SRE practice in production: SLO/SLI, error budgets, incident management, postmortems
Cloud (AWS, Azure or GCP), Kubernetes, Terraform or other IaC; Python or similar for automation
Understands how modern data platforms work and fail: Databricks, Snowflake or a cloud-native equivalent; orchestration (Airflow or similar); batch and streaming
Data reliability practice: data quality checks, freshness and volume monitoring, lineage, pipeline SLAs, cost observability
Working understanding of LLM application architecture (RAG, agents, model gateways) and what has to be measured: quality evaluation, latency, token cost, drift, guardrails
Experience instrumenting or operating at least one AI/ML workload in production, or designing such a solution for a client
Self-driven and reliable on commitments: owns deadlines for proposals and client deliverables without supervision
Comfortable switching between several presales and one delivery engagement
Uses AI assistants in daily engineering and documentation work

Буде перевагою

Vendor certifications: New Relic, Datadog, Splunk, Dynatrace; Databricks or Snowflake; cloud architect level (AWS, Azure, GCP)
Data observability tooling: Monte Carlo, Soda, Great Expectations, Databricks Lakehouse Monitoring, Unity Catalog
LLM observability and evaluation tooling: Langfuse, LangSmith, Arize, MLflow, OpenTelemetry GenAI conventions
AIOps and ITSM integration: ServiceNow, PagerDuty, event correlation engines
FinOps for observability and data platforms; licence and ingestion cost optimisation
AI security and governance basics: guardrails, red teaming, data masking
Domain experience in retail, finance or manufacturing
Public profile: conference talks, articles, community leadership