Senior Associate – AI ML Engineer

Job description

Role Overview: We are seeking an AI/ML Engineer with 3–5 years of hands-on experience building and deploying machine learning and Generative AI applications. You will contribute across the product lifecycle—from data preparation and experimentation to API development, evaluation, deployment, monitoring, and continuous improvement. The ideal candidate combines practical AI/ML knowledge with strong software-engineering discipline and a demonstrated ability to build reliable enterprise-grade solutions across multiple industries. What You Will Do - Build and maintain end-to-end AI/ML and Generative AI applications, including data pipelines, model or prompt workflows, APIs, evaluation, deployment, and monitoring. - Design Retrieval-Augmented Generation (RAG) solutions using document ingestion, chunking, embeddings, vector search, reranking, citations, and access-aware retrieval. - Develop agentic AI workflows that use tools, structured outputs, state or memory, orchestration, guardrails, human-in-the-loop approvals, and failure recovery. - Integrate foundation models and AI services from commercial and open-source ecosystems; select models based on quality, latency, cost, privacy, and deployment constraints. - Implement prompt engineering, few-shot patterns, function/tool calling, structured output validation, and—where justified—fine-tuning or parameter-efficient tuning. - Create reproducible evaluation pipelines for accuracy, relevance, groundedness, safety, latency, reliability, and cost; maintain regression or “golden” test datasets. - Develop production services using Python, REST APIs, asynchronous processing, and well-defined interfaces; write clean, modular, documented, and testable code. - Use Git and GitHub for version control, pull requests, code review, issue tracking, and release management; implement CI/CD workflows with GitHub Actions or equivalent tools. - Containerize and deploy applications using Docker and cloud services; contribute to Kubernetes-based deployments, autoscaling, secrets management, observability, and rollback strategies as needed. - Apply secure AI development practices, including privacy controls, prompt-injection defenses, authorization checks, secrets handling, content safety, auditability, and responsible AI principles. - Collaborate with product managers, data scientists, software engineers, cloud/platform teams, and business stakeholders to translate requirements into measurable technical outcomes. - Create technical documentation, architecture notes, runbooks, and knowledge-sharing materials; participate actively in design reviews, code reviews, and agile delivery ceremonies. Required Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, or a related field—or equivalent practical experience. - 3–5 years of professional experience developing software, data, or machine learning solutions, including substantial hands-on experience with Generative AI or LLM-based applications. - Strong Python programming skills and practical experience with common data and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent. - Working knowledge of LLM application patterns such as prompting, embeddings, RAG, vector databases, tool/function calling, structured outputs, and agent workflows. - Experience building and consuming REST APIs, working with JSON and schemas, and integrating databases, enterprise systems, or external services. - Understanding of software-engineering practices: object-oriented or modular design, unit and integration testing, logging, error handling, code review, documentation, and debugging. - Hands-on experience with Git/GitHub and CI/CD concepts; ability to create or maintain automated build, test, security-scan, and deployment workflows. - Experience with at least one cloud platform (AWS, Azure, or GCP) and containerization using Docker; familiarity with Kubernetes is beneficial. - A current, role-relevant AWS AI/ML certification or Microsoft Azure AI certification is mandatory. - Understanding of ML/LLM evaluation, experiment tracking, model or prompt versioning, observability, and production monitoring. - Strong analytical, communication, and collaboration skills, with the ability to explain technical trade-offs to both technical and non-technical audiences. Preferred Experience - Experience with one or more GenAI/agent frameworks or SDKs, such as OpenAI Agents SDK, LangGraph/LangChain, Semantic Kernel, LlamaIndex, AutoGen, or similar. - Experience with vector stores or search platforms such as pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch, Azure AI Search, or equivalent. - Exposure to LLMOps/MLOps tooling for experiment tracking, tracing, evaluation, model registry, prompt management, or monitoring (for example, MLflow or comparable platforms). - Knowledge of SQL and data modeling; exposure to streaming, queues, workflow orchestration, or distributed processing is a plus. - Experience applying AI to enterprise use cases such as finance, accounting, operations, customer service, document intelligence, software engineering, analytics, or workflow automation. - Awareness of responsible AI, bias and risk assessment, data governance, secure development, and regulatory or client-compliance requirements. - Open-source contributions, technical writing, hackathon projects, or a portfolio demonstrating deployed AI applications. 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 any other characteristic protected by law. Full time roles are eligible for a full range of benefits including medical, dental, and vision insurance, 401(k) with company match, and PTO. A complete description of all available benefits can be found at Riveron's Benefits page at https://riveron.com/riveron-life/. Contract roles are not eligible for benefits. FRAUD ALERT Please beware of fraudulent schemes or impersonations when going through the job application process. A Riveron employee will never recruit via text or extend unsolicited employment offers. Additionally, a Riveron employee will never ask you to exchange money or purchase anything as part of the recruiting process. ARTIFICIAL INTELLIGENCE (AI) TOOLS ARE USED TO SUPPORT THE HIRING PROCESS IN SCREENING, ASSESSING, AND/OR SELECTING APPLICANTS FOR THIS POSITION. THESE TOOLS ASSIST OUR RECRUITMENT TEAM BUT DO NOT REPLACE HUMAN JUDGMENT. FINAL HIRING DECISIONS ARE ULTIMATELY MADE BY HUMANS. IF YOU WOULD LIKE MORE INFORMATION ABOUT HOW YOUR DATA IS PROCESSED, PLEASE CONTACT US.