Manager - Senior AI/ML Engineer

Role overview

What you will do

• Own AI/ML and GenAI solutions end to end, including data pipelines, model and prompt workflows, APIs, evaluation, deployment, observability, and optimization.
• Design secure enterprise RAG platforms with ingestion, chunking, embeddings, search, reranking, citations, and access-aware retrieval.
• Build production agentic systems with tool calling, structured outputs, planning, memory, orchestration, approvals, recovery, and auditable traces.
• Architect and operate MCP clients and servers with secure transports, authentication, least-privilege access, tenant isolation, and prompt-injection protection.
• Integrate agents with enterprise repositories, source control, ticketing, databases, ERP or CRM systems, and cloud services.
• Define quality gates for accuracy, groundedness, safety, latency, and cost, and establish observability for agent and MCP activity.
• Build Python production services with GitHub CI/CD and AWS or Azure cloud-native deployment using Docker, Kubernetes, and infrastructure as code.
• Partner with stakeholders, lead architecture reviews, and mentor engineers.

Required qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
• 5–9 years developing production software, data, ML, or AI solutions, including hands-on GenAI or LLM application delivery.
• Advanced Python and practical experience with data or machine learning libraries.
• Strong understanding of LLM and agentic architecture, including prompting, context engineering, embeddings, RAG, tool calling, orchestration, evaluation, and human-in-the-loop controls.
• Experience designing APIs, distributed services, and event-driven or asynchronous workflows with secure AI-agent tool execution.
• Hands-on Git or GitHub and CI/CD experience with automated build, test, security-scan, and deployment workflows.
• Strong AWS or Azure experience with Docker; Kubernetes and infrastructure-as-code experience are expected.
• A current role-relevant AWS AI/ML or Microsoft Azure AI certification is mandatory.
• Hands-on production MCP experience is mandatory, including clients, servers, tools, resources, prompts, transports, security, approvals, testing, and tracing.
• Experience with MLOps or LLMOps practices such as versioning, tracing, evaluation, monitoring, and cost optimization.

Preferred experience

• Experience with GenAI or agent frameworks such as OpenAI Agents SDK, LangGraph, LangChain, Semantic Kernel, AutoGen, LlamaIndex, Amazon Bedrock Agents, or Azure AI Foundry Agent Service.
• Experience with enterprise search and vector technologies such as pgvector, Pinecone, Weaviate, Milvus, Elasticsearch, OpenSearch, Azure AI Search, or Amazon OpenSearch.
• Experience with MLflow or comparable LLMOps and observability platforms.
• Strong SQL and data modeling, streaming, workflow orchestration, data lake, or lakehouse experience.
• Experience leading AI solutions in finance, accounting, operations, document intelligence, analytics, or compliance.
• Familiarity with responsible AI, model risk management, data governance, privacy, and regulatory or client compliance.

About Riveron

Benefits and employment notes

• Full-time roles are eligible for medical, dental, and vision insurance, a 401(k) with company match, and paid time off.
• Contract roles are not eligible for benefits.

Hiring process note

• AI tools support screening, assessment, and selection, while final hiring decisions are made by humans.