Senior Associate – AI ML Engineer

Role Overview

What You Will Do

• Build end-to-end AI/ML and Generative AI applications, including pipelines, model or prompt workflows, APIs, evaluation, deployment, and monitoring.
• Design RAG solutions with ingestion, chunking, embeddings, vector search, reranking, citations, and access-aware retrieval.
• Develop agentic workflows with tools, structured outputs, memory, orchestration, guardrails, approvals, and failure recovery.
• Integrate foundation models and select them based on quality, latency, cost, privacy, and deployment constraints.
• Create evaluation pipelines, golden datasets, production Python services, CI/CD workflows, and Docker-based deployments.
• Apply secure AI practices and collaborate with product, data, engineering, cloud, and business teams.
• Create architecture notes, runbooks, technical documentation, and knowledge-sharing materials.

Required Qualifications

• A relevant Bachelor’s or Master’s degree, or equivalent practical experience, is required.
• Three to five years of professional experience developing software, data, or machine learning solutions is required.
• Substantial hands-on Generative AI or LLM application experience is required.
• Strong Python skills and experience with common data and ML libraries are required.
• Knowledge of prompting, embeddings, RAG, vector databases, tool calling, structured outputs, and agent workflows is required.
• Experience with REST APIs, JSON, schemas, software engineering practices, Git/GitHub, and CI/CD is required.
• Experience with at least one cloud platform and Docker is required; Kubernetes familiarity is beneficial.
• A current role-relevant AWS AI/ML or Microsoft Azure AI certification is mandatory.
• Understanding of evaluation, versioning, observability, and production monitoring is required.

Preferred Experience

• Experience with GenAI or agent frameworks such as OpenAI Agents SDK, LangGraph, LangChain, Semantic Kernel, LlamaIndex, or similar.
• Experience with vector stores or search platforms such as pgvector, Pinecone, Weaviate, Milvus, Elasticsearch, OpenSearch, or equivalents.
• Exposure to LLMOps or MLOps tooling, SQL, data modeling, streaming, queues, orchestration, or distributed processing.
• Experience applying AI to enterprise finance, accounting, operations, customer service, document intelligence, engineering, analytics, or automation use cases.
• Awareness of responsible AI, bias and risk assessment, data governance, secure development, and compliance requirements.

About Riveron