As a Big Data & GenAI Engineering Lead within our company's Data & AI Department, you will play a pivotal role in building the data and AI backbone that empowers product innovation and intelligent business decisions. You will lead the design and implementation of our companys next-generation lakehouse architecture, real-time data infrastructure, and GenAI-enriched solutions, helping drive automation, insights, and personalization at scale. In this role, you will architect and optimize our modern data platform while also integrating and operationalizing Generative AI models to support go-to-market use cases. This includes embedding LLMs and vector search into core data workflows, establishing secure and scalable RAG pipelines, and partnering cross-functionally to deliver impactful AI applications.
As a Big Data & GenAI Engineering Lead in our company you will…
Design, lead, and evolve our companys petabyte-scale Lakehouse and modern data platform to meet performance, scalability, privacy, and extensibility goals.
Architect and implement GenAI-powered data solutions, including retrieval-augmented generation (RAG), semantic search, and LLM orchestration frameworks tailored to business and developer use cases.
Partner with product, engineering, and business stakeholders to identify and develop AI-first use cases, such as intelligent assistants, code insights, anomaly detection, and generative reporting.
Integrate open-source and commercial LLMs securely into data products using frameworks such as LangChain, or similar, to augment AI capabilities into data products.
Collaborate closely with engineering teams to drive instrumentation, telemetry capture, and high-quality data pipelines that feed both analytics and GenAI applications.
Provide technical leadership and mentorship to a cross-functional team of data and ML engineers, ensuring adherence to best practices in data and AI engineering.
Lead tool evaluation, architectural PoCs, and decisions on foundational AI/ML tooling (e.g., vector databases, feature stores, orchestration platforms).
Foster platform adoption through enablement resources, shared assets, and developer-facing APIs and SDKs for accessing GenAI capabilities.
As a Big Data & GenAI Engineering Lead in our company you will…
Design, lead, and evolve our companys petabyte-scale Lakehouse and modern data platform to meet performance, scalability, privacy, and extensibility goals.
Architect and implement GenAI-powered data solutions, including retrieval-augmented generation (RAG), semantic search, and LLM orchestration frameworks tailored to business and developer use cases.
Partner with product, engineering, and business stakeholders to identify and develop AI-first use cases, such as intelligent assistants, code insights, anomaly detection, and generative reporting.
Integrate open-source and commercial LLMs securely into data products using frameworks such as LangChain, or similar, to augment AI capabilities into data products.
Collaborate closely with engineering teams to drive instrumentation, telemetry capture, and high-quality data pipelines that feed both analytics and GenAI applications.
Provide technical leadership and mentorship to a cross-functional team of data and ML engineers, ensuring adherence to best practices in data and AI engineering.
Lead tool evaluation, architectural PoCs, and decisions on foundational AI/ML tooling (e.g., vector databases, feature stores, orchestration platforms).
Foster platform adoption through enablement resources, shared assets, and developer-facing APIs and SDKs for accessing GenAI capabilities.
Requirements:
8+ years of experience in data engineering, software engineering, or MLOps, with hands-on leadership in designing modern data platforms and distributed systems.
Proven experience implementing GenAI applications or infrastructure (e.g., building RAG pipelines, vector search, or custom LLM integrations).
Deep understanding of big data technologies (Kafka, Spark, Iceberg, Presto, Airflow) and cloud-native data stacks (e.g., AWS, GCP, or Azure).
Proficiency in Python and experience with GenAI frameworks like LangChain, LlamaIndex, or similar.
Familiarity with modern ML toolchains and model lifecycle management (e.g., MLflow, SageMaker, Vertex AI).
Experience deploying scalable and secure AI solutions with proper attention to privacy, hallucination risk, cost management, and model drift.
Ability to operate in ambiguity, lead complex projects across functions, and translate abstract goals into deliverable solutions.
Excellent communication and collaboration skills, with a passion for pushing boundaries in both data and AI domains.
8+ years of experience in data engineering, software engineering, or MLOps, with hands-on leadership in designing modern data platforms and distributed systems.
Proven experience implementing GenAI applications or infrastructure (e.g., building RAG pipelines, vector search, or custom LLM integrations).
Deep understanding of big data technologies (Kafka, Spark, Iceberg, Presto, Airflow) and cloud-native data stacks (e.g., AWS, GCP, or Azure).
Proficiency in Python and experience with GenAI frameworks like LangChain, LlamaIndex, or similar.
Familiarity with modern ML toolchains and model lifecycle management (e.g., MLflow, SageMaker, Vertex AI).
Experience deploying scalable and secure AI solutions with proper attention to privacy, hallucination risk, cost management, and model drift.
Ability to operate in ambiguity, lead complex projects across functions, and translate abstract goals into deliverable solutions.
Excellent communication and collaboration skills, with a passion for pushing boundaries in both data and AI domains.
This position is open to all candidates.
