Senior Data Engineer: AI/GenAI Data Pipelines Lead

McKinsey

Atlanta, Boston, Chicago, New York (GA, MA, IL, NY)

In loco

USD 176.000 - 180.000

Tempo pieno

2 giorni fa

Candidati tra i primi

Una candidatura completa in un minuto

Curriculum e lettera di presentazione personalizzati, pronti da inviare.

Supera i filtri ATS

Vantaggi offerti da questo lavoro

Competitive salary

Comprehensive benefits package

Career growth opportunities

Descrizione del lavoro

McKinsey & Company is seeking a senior Data Engineer leading the design and implementation of scalable data architectures for cutting-edge AI systems. You will own technical workstreams, mentor engineers, and collaborate with clients to deliver enterprise-grade ML solutions, integrating Generative AI, agentic frameworks, and robust data governance across cloud environments.

You will work with cross-functional teams in a North American office context, driving feature engineering, MLOps/LLMOps

Competenze

Degree in Computer Science/Engineering, or equivalent experience.
5+ years of relevant professional experience in a data engineering role, with experience leading technical workstreams, mentoring junior engineers, and driving the adoption of software engineering best practices within a team.
Expert-level proficiency in Python and SQL and the ability to work in polyglot environments (Scala, Java) when required by client enterprise systems.
Strong experience building Agentic AI, Generative AI, Machine Learning, and Business Intelligence systems, including prompt design, retrieval-augmented generation (RAG), embeddings, vector databases, context construction, and output handling in production workflows using modern frameworks (Spark, LangChain, Databricks, Dask, Airflow, Dagster, Kedro, etc.)
Ability to lead the implementation of AI features end to end, with sound judgment around model behavior, evaluation, reliability, guardrails, and the trade-offs between quality, latency, and cost.
Experience implementing robust data security and governance controls, including managing PII/PHI, authentication, and role-based access control (RBAC).
Deep knowledge of MLOps/LLMOps including CI/CD for data workflows, automated agent evaluation (LangSmith, Opik, Langfuse), and infrastructure as code (Terraform) across cloud providers (AWS, Azure, GCP).
Exceptional time management and ability to own technical workstreams autonomously.
Experience using coding agents (Cursor, Claude Code, Codex, etc.) is a plus.
Willingness to travel as required.
Strong communication skills, both verbal and written, in English.

Mansioni

Lead technical workstreams and mentor junior engineers.
Design and deliver scalable data architectures for AI and agentic systems.
Develop robust data pipelines and govern data environments.
Architect enterprise-grade ML systems with AI features end-to-end.
Integrate ML/Generative AI capabilities and agentic frameworks into client solutions.
Design MLOps/LLMOps-focused architectures for model management, observability, and automated retraining.
Oversee feature engineering workstreams to optimize data assets for next-gen AI models.
Collaborate with clients and cross-functional teams to deliver impact at scale.