Senior AIML Data Engineer

Talent Sutra; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading technology consultancy is seeking a Principal Solution Consultant to bridge the gap between data engineering and AI/ML applications. This role requires a blend of technical expertise and executive-level storytelling. Responsibilities include leading technical discovery sessions, designing scalable architectures, and acting as a trusted advisor to key stakeholders. Ideal candidates have 8+ years of experience in data engineering and proven AI/ML deployment expertise. Flexibility for travel to client sites is required.

Qualifications

• 8+ years of experience with distributed systems and modern data stacks.
• Experience deploying ML models and deep understanding of the ML lifecycle.
• Expert-level proficiency in at least one major cloud provider.

Responsibilities

• Lead deep-dive technical discovery sessions to uncover client pain points.
• Design end-to-end data architectures and oversee their deployment.
• Act as a trusted advisor to CTOs, CDOs, and Lead Data Scientists.

Skills

Data Engineering mastery

AI/ML expertise

Cloud infrastructure knowledge

Architectural design

Cross-functional leadership

Tools

Spark

Flink

Snowflake

Databricks

Airflow

dbt

AWS

Azure

GCP

Job description

As a Principal Solution Consultant, you will be the primary technical strategist for our most complex, high-stakes enterprise engagements. You will bridge the gap between sophisticated Data Engineering architectures and cutting-edge AI/ML applications to solve real-world business challenges. This is a high-impact role requiring a blend of deep technical "hands-on-keyboard" expertise and executive-level storytelling.

Key Responsibilities

• Technical Discovery & Strategy: Lead deep-dive discovery sessions to uncover client pain points. Translate messy data landscapes into elegant, scalable AI/ML roadmaps.
• Architectural Design: Design end-to-end data architectures—from ingestion and transformation (ETL/ELT) to model deployment (MLOps) and visualization.
• Cross-Functional Leadership: Act as the "voice of the customer" for Product and Engineering teams to influence the product roadmap based on market feedback.
• Trusted Advisor: Build long-term relationships with CTOs, CDOs, and Lead Data Scientists, guiding them through the complexities of AI governance, ethics, and scalability.

Requirements

Technical Requirements

• Data Engineering (Mastery): 8+ years of experience with distributed systems (Spark, Flink), modern data stacks (Snowflake, Databricks), and orchestration tools (Airflow, dbt).
• AI/ML Expertise: Proven experience deploying ML models into production. Deep understanding of the ML lifecycle (MLOps), vector databases (Pinecone, Milvus), and fine-tuning Large Language Models (LLMs).
• Cloud Infrastructure: Expert-level proficiency in at least one major cloud provider (AWS, Azure, or GCP), specifically around data and AI services (e.g., SageMaker, Vertex AI).
• Architecture: Experience designing microservices-based architectures and API integrations.

Travel Flexibility

Flexbilit of travelling to client sites. You thrive in face-to-face environments where high-touch technical strategy and relationship building are key to executing complex deals.