Top Hybrid Machine Learning Engineer Jobs

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the architecture and operation of DAT’s shared AI and machine learning platform. Build model gateways, inference services, feature and vector storage, evaluation systems, observability, governance, and cost controls on AWS. Lead distributed-systems and cloud architecture, production incident response, engineering standards, cross-functional alignment, and mentorship. Support model training, serving, monitoring, safety, and continuous improvement across AI teams.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Leads the design, development, deployment, and governance of production machine learning and GenAI systems on Azure Databricks. Establishes MLOps standards, builds MLflow and CI/CD foundations, architects RAG and LLM solutions, monitors model performance and cost, and mentors ML and data engineers. Partners with product, risk, governance, legal, and security teams to ensure models are reliable, explainable, fair, documented, and compliant.

Provides technical leadership for full-stack engineering and applied AI initiatives. Designs and delivers resilient microservices, event-driven systems, AI-augmented workflows, and agentic solutions using cloud, orchestration, retrieval, and model technologies. Evaluates AI use cases, establishes production monitoring and governance practices, mentors engineers, influences technology strategy, and ensures solutions meet reliability, security, performance, and operational standards.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.

Build AI-training products from acquired codebases, workspaces, and databases. Develop reinforcement-learning environments, agentic task suites, evaluations, verifiers, benchmarks, and training datasets. Create reproducible pipelines using repository ingestion, test harnesses, Docker sandboxes, reward scripts, and QA tooling. Collaborate with AI labs and buyers, identify commercial opportunities, and protect sensitive data through strong security, privacy, licensing, and PII practices.

Lead development of an evaluation platform for agentic AI and LLM systems. Design benchmarking, human review, LLM-as-judge, monitoring, annotation, dataset versioning, and data quality workflows. Own technical architecture, production infrastructure, stakeholder reporting, and platform health while partnering with research, product, and platform teams. Mentor engineers and support scalable AI experimentation and deployment.

Designs and develops secure, production-grade AI solutions for government patent-data workflows. Responsibilities include building generative AI applications, LLM and RAG capabilities, reusable services, APIs, evaluation frameworks, and integrations. Provides architecture guidance, establishes responsible-AI controls, evaluates technical alternatives, mentors engineering teams, and supports government demonstrations, proposals, and stakeholder engagements. The role grows toward end-to-end AI architecture and technical strategy ownership.

Build scalable data and ML systems for LLM training, post-training, evaluation, and continuous improvement. Responsibilities include creating data pipelines, human-in-the-loop labeling workflows, synthetic datasets, quality metrics, and automated evaluation systems. The role applies LLMs and machine learning to data generation and validation, processes large-scale structured and unstructured data, translates research into production systems, and provides technical leadership and mentorship.