Consultant - Data & Artificial Intelligence

Job Description

We are seeking a Data and AI Consultant for a newly formed Data & Analytics unit serving high-technology customers on the US

West Coast. This is a senior, customer-facing role that combines advisory and solutioning leadership with practical knowledge

across Data Engineering, Data Management, conversational analytics, advanced analytics and AI. The consultant will shape

account-level Data and AI agendas, identify opportunities, develop roadmaps and work with CoE and delivery teams to take

solutions into production.

Key Responsibilities

Data and AI advisory

Build trusted relationships with customer data, analytics, AI and platform leaders; assess business and

technology priorities; and define practical transformation roadmaps.

Data Engineering and platform solutioning

Shape modern data architectures and engineering solutions covering ingestion,

transformation, lakehouse / warehouse, streaming, integration and data serving.

Data Management

Advise on governance, metadata, cataloging, quality, lineage, Master Data Management, security and privacy

to make enterprise data trusted and AI-ready.

Conversational analytics

Identify and solution natural-language analytics opportunities such as text-to-SQL, chat-based BI,

semantic search, RAG-enabled insight assistants and conversational workspaces, with appropriate governance and access

controls.

AI and GenAI solutioning

Lead discovery and design for ML, GenAI and agentic use cases; assess feasibility and value; define

target architecture; and establish evaluation, monitoring and Responsible AI guardrails.

Customer workshops

Plan and facilitate Data and AI discovery sessions, current-state assessments, architecture workshops and

use-case prioritization discussions, converting outcomes into a sequenced roadmap and qualified pipeline.

Proposals and presales

Own or contribute to RFP, RFI and proactive proposals, including solution approach, reference

architecture, estimates, delivery model, staffing and pricing inputs; present and defend recommendations to business and

technical stakeholders.

Offerings and assets

Convert recurring customer needs into reusable accelerators, PoVs, demos, reference architectures and

playbooks with Data and AI CoEs and alliance partners.

Delivery governance

Guide programs from assessment and PoC through pilot and production; review key architecture and

engineering deliverables; and ensure quality, security, observability, cost control and business outcomes.

Capability building

Define skill priorities across Data Engineering, Data Management, analytics and AI; mentor consultants,

architects and engineers; and maintain a current market and technology point of view.

Qualifications

Experience

Demonstrated senior-level experience in Data and AI consulting, solution architecture, and customer-facing technology

leadership.

Education

Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent relevant professional

experience; Master's or PhD desirable.

Must-Have Technical / Functional Skills

Data, AI and conversational analytics - core of the role

• Strong consulting and solution architecture experience spanning modern data platforms, Data Engineering, Data Management,

analytics, Generative AI and conversational analytics.

• Working knowledge of Data Engineering using Python, SQL, Spark / PySpark, ETL / ELT, data pipelines, batch and streaming

patterns, APIs, Kafka and cloud-native orchestration services.

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Data and AI Consultant | Publication-ready JD

• Working knowledge of Data Management, including data architecture, lakehouse and medallion patterns, Data Mesh, Master

Data Management, metadata, cataloging, data quality, lineage, security, privacy and governance.

• Hands-on understanding of conversational analytics and natural-language access to enterprise data, including text-to-SQL,

semantic layers, RAG, enterprise search, chat-based BI, insight generation and responsible access controls.

Deep understanding of GenAI engineering

RAG over enterprise data, prompt and context engineering, embeddings and vector

stores such as Pinecone, Weaviate or Milvus, model selection and LLM fine-tuning using LoRA / QLoRA.

• Practical experience with agentic AI patterns including planning, tool use, function calling, memory and multi-agent orchestration

using LangChain, LangGraph, Semantic Kernel or equivalent.

• Knowledge of classical ML and data science using Python, PyTorch / TensorFlow and scikit-learn, including feature engineering,

model evaluation and interpretability.

Production awareness across DataOps, MLOps and LLMOps

CI/CD, testing, observability, model and data monitoring, drift,

cost management, Docker and Kubernetes.

Cloud data and AI platform foundation

• Strong expertise in at least one cloud data and AI stack, with familiarity in a second: Google Cloud (BigQuery, Dataflow,

Dataproc, Dataplex, Vertex AI), Azure (Data Factory, Databricks, Fabric, Purview, Azure ML, Azure OpenAI) or AWS (Glue,

EMR, Redshift, SageMaker, Bedrock).

• Experience with Databricks and/or Snowflake, including lakehouse or warehouse architecture, data engineering, governance and

AI capabilities such as Mosaic AI or Cortex.

• Ability to design an AI-ready data estate with scalable ingestion, transformation, semantic and serving layers, backed by data

quality, lineage, governance and observability.

Consulting, CoE leadership and solutioning

• Proven experience leading or building a Data and AI practice, CoE or capability pod, defining offerings and assets, setting

architecture standards, and mentoring data, analytics and AI teams.

• Strong customer-facing track record in discovery and design workshops, use-case prioritization, current-state assessment,

roadmap creation and executive presentations.

• Ownership of RFP / RFI and proactive Data and AI proposals covering solution approach, target architecture, estimation, delivery

model, commercials inputs and orals.

• Experience leading initiatives from assessment and PoC through pilot and production with distributed onsite-offshore teams.

Domain context

• Experience with high-technology customers including hyperscalers, software and platform companies, semiconductor

organizations and consumer-electronics enterprises.

Generic Managerial Skills

• Ability to lead a technical capability without formal line authority - setti ng direction, standards and priorities across pods and

accounts.

• Strong consultative and communication skills - credible with principal AI engineers and confident presenting to executive

stakeholders; willing and able to travel regularly to customer locations across the US West Coast.

• Experience coordinating across geographies and time zones with offshore delivery and CoE teams; mentoring and growing

technical talent.

• Comfortable in an early-stage unit - self-directed