Spark

Infotree Global Solutions•Capon Bridge, WV

11h

About The Position

We are looking for a Senior/Lead Data & GenAI Engineer to design, build and maintain scalable, production-grade data and AI platforms. The role combines software engineering, distributed data processing, cloud-native technologies and Generative AI. You will work across teams, drive technical initiatives and build reusable libraries and frameworks that enable reliable, scalable and testable systems.

Requirements

5+ years of professional software/data engineering experience.
Strong hands-on experience with Python and/or Java.
Strong experience with Apache Spark and distributed data processing.
Experience with Databricks and/or modern lakehouse platforms.
Experience with Snowflake or comparable cloud data warehouses.
Practical experience with Kubernetes and cloud-native technologies.
Experience designing and maintaining large-scale data pipelines.
Strong understanding of distributed systems, scalability and production engineering.
Experience developing ML/AI or GenAI applications.
Experience with LLM-based applications, RAG, AI agents or LLM orchestration.
Familiarity with LangChain, LangGraph or similar GenAI frameworks.
Strong software engineering fundamentals including testing, code quality and system design.

Nice To Haves

Experience with AWS, Azure or GCP.
Experience with streaming technologies such as Kafka.
Experience with Delta Lake / Lakehouse architecture.
Experience building RAG pipelines and vector-search solutions.
Experience with LLM evaluation, observability and productionization.
Experience with AI agents, tool calling and multi-step workflows.
Experience building internal developer platforms, frameworks or reusable engineering libraries.
Experience leading cross-functional or cross-team technical initiatives.

Responsibilities

Develop, test and maintain high-quality, production-ready software.
Design and implement large-scale data pipelines and distributed processing systems.
Build scalable cloud-native services and platforms using modern engineering practices.
Provide technical leadership for cross-team initiatives and complex engineering projects.
Design and develop reusable libraries, frameworks and platform components.
Optimize distributed data processing workloads for performance, scalability and reliability.
Work with data platforms including Databricks, Apache Spark and Snowflake.
Develop and deploy applications using Python and/or Java.
Build and operate containerized workloads using Kubernetes and cloud-native technologies.
Design and implement GenAI/LLM-based applications and services.
Work with frameworks such as LangChain and LangGraph for LLM orchestration and agentic workflows.
Collaborate with data scientists, software engineers, architects and product teams.
Establish engineering best practices around testing, observability, reliability and deployment.