Technical Architect - MLE in United States Embassy at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Technical Architect - MLE based in the United States.

This role is an opportunity for an experienced Machine Learning Engineer to design and build next-generation AI systems powered by agentic workflows and multi-agent architectures. You will lead the development of production-grade AI solutions that can reason, collaborate, and execute complex tasks with minimal human intervention. Working at the intersection of machine learning, cloud engineering, and generative AI, you will architect scalable platforms that deliver measurable business impact. The position requires deep technical expertise, innovation, and the ability to transform advanced AI concepts into reliable enterprise solutions. You will collaborate with talented engineers and technical stakeholders in a fast-paced environment focused on AI transformation. This is an ideal opportunity for a senior AI professional passionate about shaping the future of intelligent systems.

• Architect and develop end-to-end agentic AI systems and multi-agent workflows from concept to production.
• Design agent architectures, including orchestration layers, communication protocols, agent roles, task planning mechanisms, and collaboration frameworks using technologies such as CrewAI, LangGraph, and AutoGen.
• Build advanced agent capabilities through custom tools, agent skills, integrations, and domain-specific workflows.
• Develop state management, memory systems, and context engineering approaches that enable persistent, reliable, and intelligent agent interactions.
• Deploy, scale, and maintain production-grade AI solutions across major cloud platforms including AWS, Google Cloud Platform, and Azure.
• Implement MLOps best practices, including monitoring, CI/CD, reliability improvements, and operational excellence.
• Integrate and optimize large language models using techniques such as Retrieval-Augmented Generation (RAG), prompt engineering, and parameter-efficient fine-tuning (PEFT).
• Create and maintain enterprise tool libraries, API integrations, database connections, and external service integrations for AI agents.
• Design and implement scalable RAG solutions using vector databases such as Pinecone, Weaviate, and ChromaDB.
• Build observability and evaluation frameworks to measure agent performance, reliability, accuracy, cost, and latency.
• Implement monitoring and tracing solutions using tools such as LangSmith, Arize AI, or custom telemetry platforms.
• Establish quality metrics, evaluation processes, safety controls, and performance standards for production AI systems.
• Apply best practices for AI safety, including prompt injection prevention, data privacy protection, retrieval security, and responsible AI deployment.
• Collaborate with technical teams and stakeholders to deliver innovative AI solutions while mentoring engineers and promoting technical excellence.

• 8–12 years of professional experience in machine learning engineering, AI architecture, or related technical roles, with demonstrated experience delivering ML systems into production.
• Proven expertise designing and implementing multi-agent systems and agentic AI workflows.
• Strong programming skills in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, and Transformers.
• Experience developing scalable applications using FastAPI, asynchronous programming, and microservices architectures.
• Hands-on experience building RAG systems and working with vector databases including Pinecone, Weaviate, or ChromaDB.
• Strong knowledge of LLM application monitoring, evaluation, and observability tools such as LangSmith, Weights & Biases, or similar platforms.
• Experience architecting complex AI systems from the ground up in enterprise production environments.
• Production-level experience with at least one major cloud platform: AWS, Google Cloud Platform, or Azure.
• Knowledge of cloud infrastructure including compute services, serverless functions, container orchestration platforms, and managed AI/ML services.
• Strong DevOps expertise including Infrastructure as Code tools such as Terraform or CloudFormation, CI/CD pipelines, Docker, and Kubernetes.
• Familiarity with distributed systems, message queues, event-driven architectures, and stateful AI orchestration patterns.
• Experience with AI evaluation methodologies, including trajectory analysis, tool-use validation, regression testing, and LLM-based evaluation frameworks.
• Understanding of AI safety practices, including data handling, guardrails, access controls, and secure prompt engineering.
• Knowledge of model lifecycle management, including routing strategies, model versioning, fallbacks, and optimization techniques.
• Strong problem-solving and analytical skills with the ability to solve complex technical challenges.
• Excellent communication skills with the ability to explain advanced AI concepts to both technical and non-technical audiences.
• Ability to work independently, lead large-scale initiatives, and mentor other engineers.
• Experience with agile methodologies, software development lifecycle practices, and version control systems such as Git.

• Remote work opportunity within the United States.
• Opportunity to work on cutting-edge AI, machine learning, and cloud transformation initiatives.
• Collaborative environment focused on innovation, learning, and professional growth.
• Exposure to advanced generative AI, agentic AI, and enterprise-scale technology solutions.
• Opportunities to collaborate with global teams and industry-leading technology partners.
• Career development opportunities within a rapidly growing AI-focused organization.
• Inclusive culture built around transparency, diversity, integrity, and continuous learning.
• Opportunity to contribute to impactful solutions addressing complex business challenges.