Senior GCP ML Engineer

OpenKyber LLC
United States
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) JavaScript (Programming Language) Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Microsoft Azure Code Review Software Design Patterns Distributed Systems JSON Python (Programming Language) Automation of Marketing
+18 more
Enterprise Messaging Systems Performance Tuning Simple Object Access Protocol (SOAP) Software Engineering Systems Integration TypeScript YAML Data Logging Google Cloud Large Language Models Multi-Agent Systems Prompt Engineering SOAPAPI Backend Machine Learning Operations Cloud Integration Terraform Microservices

Job description

Own the endtoend architecture for the AIagent, DSL, and SFMC automation ecosystem. Design agentic AI systems, backend microservices, APIs, and SFMC integrations (REST/SOAP). Define DSL schemas (JSON/YAML) for AIgenerated workflows, ensuring extensibility, safety, and deterministic execution. Establish guardrails, validation, simulation, and compliance frameworks for AIgenerated journeys and campaigns. Create and maintain system blueprints, including data flow diagrams, integration contracts, and deployment architecture. Technical Leadership

Act as the handson technical lead, guiding AI/ML engineers, DSL engineers, backend developers, and SFMC specialists. Lead POCs, prototypes, and architectural spikes to validate design decisions and technology choices. Drive coding standards, design patterns, and best practices across engineering teams. Conduct architectural reviews, code reviews, and design walkthroughs. Unblock teams, make technical decisions, and ensure alignment with architectural vision. AI/ML & Agentic Systems

Partner with AI/ML teams on: Agent frameworks (Agent SDK, LangChain, LangGraph, CrewAI, Semantic Kernel) RAG pipelines, embeddings, and vectorization LLM finetuning, evaluation, and safety mechanisms Define prompting strategies, context engineering, and modelinteraction patterns. Backend, Cloud & Integration Architecture

Architect cloudnative, highly available systems on AWS using IaC (Terraform). Oversee backend microservices, orchestration layers, and execution pipelines. Ensure robust integration with SFMC components: Journey Builder Email Studio Data Extensions Personalization logic REST/SOAP APIs Ensure observability, monitoring, logging, and reliability across all services. Security, Governance & Compliance

Ensure compliance with security, privacy, and governance requirements for AIgenerated marketing workflows. Define architectural controls for safe execution, auditability, and data protection. Lead performance optimization, scalability planning, and risk mitigation. CrossFunctional Collaboration

Work closely with business, product, CRM, and marketing operations teams to translate requirements into scalable technical solutions. Communicate architectural decisions clearly to both technical and nontechnical stakeholders.

Requirements

Do you have experience in System design?, 10+ years of software engineering experience with at least 3+ years in a Tech Lead or Architect role. Strong background in AI/ML systems, including: LLMs Agentic architectures Prompt engineering RAG pipelines Experience designing complex distributed systems and workflow automation platforms. Deep understanding of DSL design, interpreters, ASTs, and compiler concepts. Strong proficiency in Python, TypeScript, or Java. Experience with cloudnative architectures (AWS/Azure/Google Cloud Platform), containers, and microservices. Proven ability to lead engineering teams, conduct design reviews, and drive technical decisions. Excellent communication and stakeholder management skills., Experience building AIdriven workflow automation or autonomous agent systems. Familiarity with AMPscript and SSJS. Background in marketing automation, CRM systems, or customer lifecycle design. Experience with security, compliance, and governance for AI systems. Prior experience in fixedbid or outcomebased delivery environments. Experience with eventdriven architectures and messaging systems.

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