Top AI Engineer Jobs

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.

Lead design and build of New York Life's enterprise AI platform and agentic solutions. Work across cloud-native infrastructure, model and agent lifecycle, RAG knowledge systems, multi-agent orchestration, MLOps/LLMOps, governance, CI/CD, observability, and production operations. Partner with security, legal, and architecture to embed responsible AI and deliver production-ready, compliant AI systems.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Leads the architecture, development, and operation of a scalable AI/ML platform supporting deep learning, LLMs, agentic AI, MLOps, and LLMOps. Optimizes compute and storage resources, builds reusable APIs and frameworks, manages data pipelines, and establishes deployment, monitoring, evaluation, governance, security, and responsible AI standards. Provides technical leadership, mentors engineering and data science teams, and drives cross-functional platform improvements.

Build and productionize a federated AI platform for agentic workflows and multi-step LLM systems. Responsibilities include designing orchestration, routing, memory, evaluation, retrieval, and user interface components; integrating emerging GenAI techniques; improving quality through evaluations and feedback loops; partnering with product, engineering, and quantitative research teams; and delivering observable, scalable, secure production software.

Designs and executes compliance, validation, and reliability tests for HPC-AI mechanical and thermal hardware, electronics enclosures, and production tooling. Responsibilities include testing tolerances, form/fit/function, shock and vibration, electromagnetic interference, safety, acoustics, system power, and thermal performance. The role collaborates with internal and outsourced engineering partners to develop reliable, cost-effective hardware solutions and applies mechanical design, CAD, CFD, and analytical problem-solving skills.

Build and own full-stack features using Java, Spring Boot, and React or Angular. Develop resilient services and Kafka integrations, improve CI/CD, testing, observability, monitoring, and production readiness, and reduce incidents. Work with SQL and NoSQL databases, AWS or Cloud Foundry, and secure engineering practices. Mentor engineers, lead design and quality practices, and apply enterprise-authorized AI-assisted development tools responsibly. Experience with LLM integrations, agentic workflows, RAG, and tool calling is preferred.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and reliability. Own complex AI architecture, define reliability objectives, improve GPU/TPU utilization, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

Build and maintain scalable data pipelines, data applications, planning tools, APIs, and automated quality processes for network capacity planning. Develop ETL/ELT workflows, anomaly detection, monitoring, metadata and lineage solutions, and machine learning or AI-enabled applications. Analyze data, optimize performance, document systems, and collaborate with engineering, product, operations, software development, and data science teams to deliver reliable production solutions.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Build and deploy production-grade LLM agents and RAG pipelines that detect, triage, and remediate platform and application issues. Design orchestration, tool calling, guardrails, evaluation frameworks, observability integrations, and secure AI infrastructure. Optimize prompts, models, retrieval, cost, latency, and quality while partnering with SRE, InfoSec, and platform teams. Develop APIs, testing workflows, and reusable enterprise AI components.

Designs and delivers agentic AI solutions that transform complex business processes into autonomous, scalable systems. Responsibilities include process decomposition, AI architecture, agent workflow development, prototyping, prompt and context strategy, integrations, evaluation, security controls, production delivery, value measurement, and mentoring. The role collaborates with business, IT architecture, security, platform engineering, and project teams to implement trusted enterprise generative AI solutions.

Designs and scales enterprise data platforms, pipelines, and integration frameworks for structured and unstructured data. Builds Snowflake and cloud-based AWS/GCP solutions, leads legacy-platform modernization and ETL/ELT implementation, and develops AI-ready datasets and data products. Establishes data governance, security, monitoring, and CI/CD practices for reliable, compliant platforms supporting analytics, machine learning, generative AI, and automation.

Build and operate security tools, guardrails, detection capabilities, and automated response systems that protect AI agents, LLM applications, model-backed services, and supporting infrastructure. The role addresses prompt injection, tool abuse, credential misuse, data exfiltration, and agent manipulation while developing reusable architectures and frameworks. It partners with security, threat intelligence, platform engineering, and AI teams to operationalize defenses and guide technical strategy.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Own the distributed serving and data infrastructure powering globally served pages and governed multi-agent build loops. Design caching, indexing, rebuild, reliability, observability, performance, and invariant-based enforcement systems. Take end-to-end responsibility for correctness, uptime, failure handling, latency budgets, and production incidents without a dedicated operations team. The role requires independently modeling complex systems, proving correctness under load, and evolving infrastructure as AI capabilities change.

Lead end-to-end delivery of cloud-native, full-stack software initiatives, defining architecture, engineering standards, reliability practices, observability, CI/CD, and secure production operations. Build microservices, APIs, and AI capabilities using LLMs and generative AI. Guide responsible AI-assisted development, lead incident response and continuous improvement, mentor engineers, and align technical priorities across stakeholders and initiatives.

Leads development of full-stack, cloud-native software using Java, Python, and AWS. Builds secure microservices, APIs, and LLM-powered capabilities; establishes engineering, testing, reliability, observability, and CI/CD standards; mentors engineers; drives responsible AI-assisted development; and manages production operations, incident response, and technical direction across agile initiatives.

Provides hands-on technical leadership for enterprise Data Trust platforms. Designs and operates distributed systems supporting data contracts, metadata, lineage, quality, privacy, access controls, reliability, and semantic context for analytics and AI. Leads batch and streaming capabilities across Snowflake, AWS, and Kafka; establishes observability, SLOs, automated enforcement, and operational rigor. Drives cross-functional architecture and multi-quarter initiatives while mentoring engineers and building reusable platform standards.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.