Platform Engineer

Job Summary

Were looking for a Senior Platform Engineer with a strong foundation in data architecture, distributed systems, and modern cloud-native platforms to architect, build, and maintain intelligent infrastructure and systems that power our AI, GenAI and data-intensive workloads.

Youll work closely with cross-functional teams, including data scientists, ML & software engineers, and product managers & play a key role in designing a highly scalable platform to manage the lifecycle of data pipelines, APIs, real-time streaming, and agentic GenAI workflows, while enabling federated data architectures. The ideal candidate will have a strong background in building and maintaining scalable AI & Data Platform, optimizing workflows, and ensuring the reliability and performance of Data Platform systems.

Responsibilities

Platform & Cloud Engineering

• Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow/Spark
• Design and develop event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent messaging systems
• Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents
• Architect and manage multi-cloud and hybrid cloud platforms (e.g., GCP, AWS, Azure) optimized for AI, ML, and real-time data processing workloads
• Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation
• Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering

Data Architecture & Governance

• Lead initiatives in data modeling, semantic layer design, and data cataloging, ensuring data quality and discoverability across domains
• Implement enterprise-wide data governance practices, schema enforcement, and lineage tracking using tools like DataHub, Amundsen, or Collibra
• Guide adoption of data fabric and mesh principles for federated ownership, scalable architecture, and domain-driven data product development

AI & GenAI Platform Integration

• Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience
• Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps
• Experience in developing and integrating GenAI applications using MCP and orchestration of LLM-powered workflows (e.g., summarization, document Q&A, chatbot assistants, and intelligent data exploration)
• Hands-on expertise building and optimizing vector search and RAG pipelines using tools like Weaviate, Pinecone, or FAISS to support embedding-based retrieval and real-time semantic search across structured and unstructured datasets

Engineering Enablement

• Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices
• Streamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfaces
• Collaborate across teams to enforce cost, reliability, and security standards within platform blueprints
• Work with engineering by introducing platform enhancements, observability, and cost optimization techniques
• Foster a culture of ownership, continuous learning, and innovation

Qualifications

• 5+ years of hands-on experience in Platform or Data Engineering, Cloud Architecture, AI Engineering roles
• Strong programming background in Java, Python, SQL, and one or more general-purpose languages
• Deep knowledge of data modeling, distributed systems, and API design in production environments
• Proficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub/Sub, Flink, Spark)
• Experience with metadata management, data catalogs, data quality enforcement, and semantic modeling & automated integration with Data Platform
• Proven experience building scalable, efficient data pipelines for structured and unstructured data
• Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation
• Experience with RAG pipelines, vector databases, and embedding-based search
• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack
• Experience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools
• Prior implementation of data mesh or data fabric in a large-scale enterprise
• Experience with Looker Modeler, LookML, or semantic modeling layers

Why Youll Love This Role

• Drive technical leadership across AI-native data platforms, automation systems, and self-service tools
• Collaborate across teams to shape the next generation of intelligent platforms in the enterprise
• Work with a high-energy, mission-driven team that embraces innovation, open-source, and experimentation