Manager - Senior AI/ML Engineer

Role details

Role Overview We are seeking a Senior AI/ML Engineer with 5–9 years of experience designing, building, and operating production-grade AI and Generative AI solutions. You will provide hands-on technical leadership across solution architecture, data and model pipelines, agentic systems, evaluation, cloud deployment, and observability. The ideal candidate pairs deep AI/ML expertise with strong software-engineering discipline and sound architectural judgment, and can lead delivery for complex enterprise use cases. What You Will Do - Own AI/ML and GenAI solutions end to end — data pipelines, model and prompt workflows, APIs, evaluation, deployment, observability, and continuous optimization. - Design enterprise RAG platforms: secure ingestion, chunking, embeddings, hybrid/vector search, reranking, citations, and access-aware retrieval. - Build production agentic systems using tool/function calling, structured outputs, planning and memory, multi-agent orchestration, human-in-the-loop approvals, failure recovery, and auditable traces. - Architect and operate MCP clients and servers that expose enterprise tools, resources, and prompts — with secure transports (stdio, Streamable HTTP), authentication, least-privilege access, tenant isolation, and protection against prompt injection and unsafe tool execution. - Integrate agents with enterprise systems (document repositories, source control, ticketing, databases, ERP/CRM, cloud services) through reusable connectors and governance patterns. - Define evaluation strategies and quality gates for accuracy, groundedness, safety, latency, and cost, and establish end-to-end observability for agent and MCP activity. - Build production services in Python with strong engineering practices, GitHub-based CI/CD, and cloud-native deployment on AWS or Azure using Docker, Kubernetes, and infrastructure as code. - Partner with product, architecture, data science, security, and business stakeholders; lead design and architecture reviews and mentor engineers. Required Qualifications - Bachelor's or Master's 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 delivery of GenAI/LLM applications. - Advanced Python and practical experience with data/ML libraries (pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent). - Strong grasp of LLM and agentic architecture: prompting, context engineering, embeddings, RAG, tool/function calling, structured outputs, orchestration, evaluation, and human-in-the-loop controls. - Proven experience designing APIs, distributed services, and event-driven or asynchronous workflows with secure tool execution for AI agents. - Hands-on experience with Git/GitHub and CI/CD, including automated build, test, security-scan, and deployment workflows. - Strong experience with AWS or Azure and containerized deployment using Docker; Kubernetes and infrastructure-as-code experience expected. - A current, role-relevant AWS AI/ML certification or Microsoft Azure AI certification is mandatory. - Hands-on production experience with Model Context Protocol (MCP) is mandatory — consuming and developing MCP servers, integrating clients with agent frameworks, defining tools/resources/prompts, managing stdio or Streamable HTTP transports, and implementing security, approvals, testing, and tracing. - MLOps/LLMOps practices: experiment tracking, model and prompt versioning, tracing, evaluation, monitoring, and cost optimization. Preferred Experience - GenAI or agent frameworks such as OpenAI Agents SDK, LangGraph/LangChain, Semantic Kernel, AutoGen, LlamaIndex, Amazon Bedrock Agents, or Azure AI Foundry Agent Service. - Enterprise search and vector technologies (pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch, Azure AI Search, Amazon OpenSearch, or equivalent). - LLMOps/observability platforms (MLflow or comparable) for tracing, evaluation, prompt management, and governance. - Strong SQL and data modeling; experience with streaming, workflow orchestration, data lakes, or lakehouse platforms. - Leading AI solutions in enterprise domains such as finance, accounting, operations, document intelligence, analytics, or compliance. - Responsible AI, model risk management, data governance, privacy, and regulatory/client-compliance familiarity. - Mentoring engineers, defining technical standards, and contributing to reusable platforms or open-source work. About Riveron: At Riveron, we partner with clients—from global multinationals to high-growth private entities—to solve complex finance challenges, guided by our DELTA values: Drive, Excellence, Leadership, Teamwork, and Accountability. Our entrepreneurial culture thrives on collaboration, diverse perspectives, and delivering exceptional outcomes. We are committed to fostering growth, both for our clients and our people, through mentorship, integrity, and a client-centric approach. This inclusive environment offers flexibility, progressive benefits, and meaningful opportunities for impactful work that supports well-being in and out of the office. Want to stay connected with Riveron? Join our Talent Community https://jobs.ashbyhq.com/riveron/form/talent-community-form to learn more about our growing firm and be considered for future opportunities. Check us out on social media: LinkedIn https://www.linkedin.com/company/riveron/life/culture/ Glassdoor https://www.glassdoor.com/Photos/Riveron-Office-Photos-E672242.htm Instagram https://www.instagram.com/riveronconsulting/ Facebook https://www.facebook.com/RiveronConsulting/ Riveron Consulting is an Equal Opportunity Employer and believes that we are stronger together through our diversity. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, disability status, protected veteran status, sexual orientation, gender identity or a