Responsibilities
- Design, build, and optimize data and AI solutions for enterprise clients
- Contribute to the development and operationalization of data pipelines, AI models, and intelligent services in production environments
- Support the implementation of scalable architectures across cloud and multi-cloud ecosystems
- Develop and improve batch and streaming data processing pipelines using modern distributed frameworks
- Build and enhance CI/CD pipelines, automation practices, and release processes for data and AI workloads
- Contribute to data lake, ingestion, transformation, and export architectures supporting business-critical use cases
- Troubleshoot production issues and improve platform reliability, performance, and operational efficiency
- Collaborate with cross-functional teams to translate technical requirements into robust, maintainable, and scalable enterprise solutions
Preferred qualifications
- Experience with LLM integration, LLMOps, or AI model deployment in enterprise applications
- Familiarity with frameworks and tools such as Ray or similar environments for model serving and orchestration
- Exposure to computer vision, fraud detection, or intelligent automation use cases
- Experience working in multi-cloud client environments
- Background spanning both software engineering and data platform delivery
Benefits
- The opportunity to work on high-impact transformation initiatives at the intersection of data engineering, AI deployment, cloud infrastructure, and agentic solutions
- Exposure to challenging enterprise environments with real engineering responsibility and end-to-end platform impact
- A role within a business unit focused on business insights, AI-driven transformation, and agentic solutions
- Professional growth in a company with a business-first, technology-agnostic mindset
- A collaborative environment focused on innovation, engineering quality, and long-term value creation
- Proven experience in roles such as Data Engineer, AI Engineer, ML Engineer, Platform Engineer, or Senior Software Engineer
- Strong hands-on background with Python, Java, or Scala
- Solid experience with cloud platforms, especially Azure, AWS and GCP
- Experience with data engineering and distributed processing ecosystems such as Spark, Kafka, Hadoop, Hive, or NiFi
- Strong knowledge of containerization, orchestration, and DevOps tools, including Docker, Kubernetes, Jenkins, or similar technologies
- Familiarity with SQL / NoSQL technologies and modern data architectures
- Experience in deployment, optimization, and troubleshooting of production-grade data or AI workloads
- Strong problem-solving skills, ownership, and a collaborative engineering mindset
Tags & focus areas
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