Data Engineer AI Java/Python/Spark

Role Overview:

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.

Key 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.

Required Experience:

• 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 Have:

• 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.

Ideal Candidate Profile:

The strongest candidate is not purely a Data Engineer and not purely an ML Engineer.

We are looking for someone who combines:

Software Engineering Data Engineering Cloud/Platform Engineering GenAI

Typical backgrounds may include

• Senior Data Engineer

• Lead Data Engineer

• Senior Software Engineer – Data

• Data Platform Engineer

• Senior Cloud Data Engineer

• AI/ML Platform Engineer

• Senior ML Engineer with strong data engineering experience

• GenAI Engineer with strong distributed-data/platform experience

• Data & AI Architect / Technical Lead

Core Technology Stack:

Languages: Python, Java

Data: Apache Spark, Databricks, Snowflake, Delta Lake

Cloud/Platform: Kubernetes, Docker, AWS/Azure/GCP, cloud-native technologies

GenAI/ML: LLMs, RAG, LangChain, LangGraph, AI agents, vector search

Engineering: Distributed systems, APIs, CI/CD, automated testing, observability, scalability