Top AI Engineer Jobs

Lead the architecture and delivery of secure data pipelines, APIs, integrations, and governed data services supporting AI assistants, enterprise search, and agent workflows. Ensure permissions, information barriers, data quality, traceability, and resilient synchronization across enterprise systems. Lead technical delivery, troubleshoot production issues, evaluate retrieval and integration performance, review designs and code, mentor engineers, and translate legal, business, security, and governance requirements into scalable solutions.

Lead security investigations for AI systems used in scientific discovery. Develop threat models, test defenses through adversarial assessments and technical analysis, define security requirements, evaluate evidence, and drive improvements with Security, Safety, and engineering teams. The role addresses dangerous misuse, model and data compromise, deployment risks, and safeguards for increasingly capable AI systems.

Build and maintain software, services, data pipelines, and model integrations for AI safety evaluations, red teaming, mitigation experiments, and scientific AI research. Translate researcher needs into reliable tools, preserve reproducible experiment records, and own systems from prototyping through deployment, testing, monitoring, documentation, and improvement. Collaborate with AI and software engineering teams on shared infrastructure.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.

Build and operate Curinos’ Databricks-native AI and ML platform. Responsibilities include developing deployment tooling, CI/CD pipelines, infrastructure-as-code, observability, governance, model monitoring, LLM evaluation frameworks, and scalable inference systems. The role partners with data scientists and engineers to productionize ML models and AI applications while supporting reliability, incident response, reproducibility, auditability, and operational excellence in a regulated FinTech environment.

Develop and maintain enterprise-scale full-stack and platform services for Honeywell Forge and Cognition. Responsibilities include building containerized microservices, REST APIs, event-driven services, distributed systems, cloud and edge deployments, database integrations, automated testing, CI/CD, observability, troubleshooting, and secure software for Kubernetes, multi-cloud, restricted-connectivity, and air-gapped environments. Collaborate with architects, SRE, cybersecurity, QA, product, and engineering teams while owning features from design through production readiness.

Build and operate secure, production-grade generative AI applications for federal missions. Responsibilities include developing agentic workflows and RAG systems, integrating cloud AI services, evaluating models, designing prompts and guardrails, implementing observability and SRE practices, optimizing cost and latency, and delivering reusable SDKs, CI/CD templates, Terraform modules, and evaluation tooling for restricted or air-gapped environments.

Leads development of scalable full-stack applications using Java, Angular, microservices, and event-driven architectures. Integrates and deploys agentic AI, GenAI, and LLM-enabled systems for M&A use cases in a regulated enterprise environment. Responsibilities include architecture, coding, testing, CI/CD, security, operational support, environment readiness, risk management, mentoring, code reviews, and stakeholder collaboration.

Partners with business teams to identify valuable AI opportunities and translate operational challenges into responsible, measurable solutions. Designs, builds, tests, evaluates, and improves AI agents, copilots, automations, and workflow integrations. Develops prototypes and production-ready solutions, establishes success metrics, monitors performance, supports adoption through training and documentation, and collaborates with data, technology, security, legal, and risk stakeholders.

Designs, develops, and supports AI-enabled business process automation across the organization. Partners with business and technical stakeholders to identify inefficiencies, define requirements, and deliver end-to-end automation solutions. Builds secure enterprise AI, LLM, agent, knowledge management, workflow, and integration capabilities, including private and on-premises deployments. Evaluates emerging technologies, establishes governance standards, and provides ongoing troubleshooting, maintenance, documentation, and improvement.

Build AI-powered business applications integrating AI models with APIs, company data, tools, MCP servers, and human review. Gather stakeholder requirements, prototype and test agent workflows, evaluate AI reliability, deploy maintainable solutions, measure business impact, and collaborate with analytics, IT, cybersecurity, and continuous improvement teams.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Lead design, implement, and manage scalable AWS/Azure cloud infrastructure to support Generative AI. Collaborate with data scientists to deploy and optimize models, use Terraform for IaC, ensure security and performance, mentor engineers, troubleshoot deployments, and document best practices.

Develop and support AI-powered applications, automation solutions, and agent-based systems for business operations. Responsibilities include software development, integrations, Azure AI configuration, RAG and prompt engineering, testing, architecture participation, monitoring, troubleshooting, documentation, security collaboration, code reviews, and user enablement. The role works cross-functionally with engineers, stakeholders, legal teams, data professionals, and technology partners to deliver scalable and reliable AI solutions.

Design, build, evaluate, and deploy scalable AI models for insurance business problems. Analyze large datasets, validate models, monitor performance, and collaborate with data scientists and engineers to align solutions with business objectives and communicate results to non-technical stakeholders.

Build production AI systems combining voice interfaces, browser agents, computer vision, and large language models. Responsibilities include developing speech-to-text and text-to-speech systems, browser automation, prompt and LLM workflows, multimodal pipelines, evaluation frameworks, real-time infrastructure, observability, and production performance improvements.

Lead ASIC verification for an AI inference chiplet from subsystem through full-chip validation. Build UVM testbenches, constrained-random tests, reference models, scoreboards, and coverage-driven verification flows. Develop C/C++ tests on RISC-V, apply formal verification, support FPGA prototyping on Synopsys HAPS, automate regressions, and use AI workflows for test generation, triage, log analysis, and coverage closure.

Leads enterprise AI enablement by evaluating and integrating LLM platforms, developing prompt and orchestration patterns, automating implementation and testing workflows, and establishing secure PHI/PII safeguards. Partners with architecture, development, security, and implementation teams to move prototypes into production, train staff, measure adoption, and improve data onboarding. Requires hands-on LLM engineering, AI coding assistant governance, data engineering, healthcare compliance, and mentorship expertise.

Design, build, and deploy production-grade agentic AI systems for regulated banking use cases. Responsibilities include multi-agent orchestration, language-model fine-tuning, evaluation frameworks, MCP and A2A integrations, enterprise API development, cloud deployment, observability, and rapid prototyping. The role requires independently delivering tested, production-ready systems while collaborating with researchers, data scientists, and business stakeholders.

Build unified structured and unstructured data pipelines, predictive and prescriptive analytics, churn models with quantified uncertainty, and production LLM agents. Own enterprise AI architectures, RAG systems, MLOps and LLMOps standards, evaluation, monitoring, cost optimization, security, and reliability. Partner with business stakeholders to translate ambiguous problems into scalable AI solutions and document reference architectures adopted by other teams.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Designs, operates, and scales Kubernetes-based data and AI infrastructure across public cloud environments. Builds platforms for Kafka, Spark, Flink, AI gateways, model serving, traffic management, observability, and self-service deployment. Partners with engineering teams to improve reliability, scalability, performance, cost efficiency, and operational simplicity of distributed systems.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Leads full-stack application development for BNY’s AI Hub Engineering team. Responsibilities include designing, developing, testing, integrating, securing, modernizing, and maintaining scalable front-end and back-end systems. The role advises business and IT leaders, resolves complex technical issues, supports enterprise technology strategies, conducts code reviews, mentors engineers, manages technical debt, and presents technology solutions to stakeholders.

Architects and leads production AI products, including chatbots, document processing, agentic workflows, and centralized AI platforms. Designs model routing, vector search, retrieval, context engineering, MCP integrations, and scalable cloud-native systems. Integrates AI with accounting systems, repositories, APIs, and enterprise technologies while ensuring monitoring, compliance, and observability. Provides technical leadership, establishes AI development standards, and mentors engineers.