Top AI & Machine Learning Jobs

Build and operate secure enterprise AI capabilities, including MCP gateways, AI-to-data integrations, RAG, agents, LLMs, vector stores, and governance controls. Implement data access policies, masking, lineage, monitoring, guardrails, logging, and compliance evidence. Integrate Databricks, Copilot, Claude, APIs, and enterprise data sources while mitigating sensitive-data risks. Provide infrastructure support, troubleshooting, performance monitoring, package deployment, escalation support, and cross-functional collaboration with Data, Architecture, Cybersecurity, Risk, and AI teams.

Own and optimize Netpreme’s production LLM inference infrastructure, including vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, Kubernetes deployments, benchmarking, distributed serving, KV caching, batching, routing, and scheduling. The role focuses on improving latency, throughput, scalability, reliability, and efficiency across diverse LLM and multimodal workloads while partnering with hardware, model, and systems engineers.

Build and optimize machine learning models for pharmaceutical commercial use cases, including patient journeys, treatment progression, churn prediction, HCP targeting, and next-best-action recommendations. Develop transformer, LSTM/GRU, and attention-based sequence models; integrate complex healthcare and commercial datasets; and create end-to-end pipelines using Databricks, PySpark, SQL, and MLflow. Translate analytical findings into actionable insights for brand, sales operations, and commercial stakeholders.

Build Python-based agentic AI platforms and integration frameworks for a proprietary data lake. Develop MCP servers, reusable AI tools, enterprise APIs, safety guardrails, testing pipelines, and Kubernetes deployments. Integrate commercial and open-source LLMs with internal databases, microservices, and APIs while addressing privacy, prompt injection, output validation, and scalability. The role also requires technical leadership, mentorship, troubleshooting, and collaboration across teams.

Develop Python-based agentic AI platform components for a proprietary data lake tool. Deploy and scale applications in Kubernetes, build MCP servers and reusable AI tools, integrate internal APIs and commercial or open-source LLMs, and implement enterprise guardrails for privacy, security, prompt injection defense, and output validation. The role also involves automated testing, troubleshooting, cross-functional collaboration, and supporting Java API interoperability in a hybrid Columbia, Maryland environment.

Leads Linktree’s data science and analytics organization, setting strategy across analytics, experimentation, applied ML, and AI. Oversees technical execution, data foundations, forecasting, experimentation infrastructure, governance, monitoring, and production systems. Partners with executives, Product, Engineering, and Design; recruits and mentors leaders; drives data-informed culture and AI adoption; and contributes hands-on to complex technical initiatives and user-facing capabilities.

Lead the architecture, development, and deployment of the NerveCenter cognitive AI platform. Design cognitive models, multi-agent workflows, RAG pipelines, and secure integrations with enterprise systems. Serve as the technical authority for LLMs, deep learning, stateful agents, and human-machine teaming while mentoring engineering and data science teams. Establish testing and safety frameworks addressing latency, hallucinations, token usage, and prompt-injection vulnerabilities in secure government and defense environments.

Build and deploy production-grade AI applications and agentic solutions for clients and Oliver Wyman. Responsibilities include full-stack development, agent orchestration, retrieval and knowledge pipelines, cloud architecture, enterprise integrations, security, evaluation, monitoring, and continuous improvement. The role collaborates with product owners, consultants, technical teams, and clients while translating business objectives into reliable AI products.

Build and evaluate emerging AI technologies through rapid proofs of concept, recommend adoption, and productionize capabilities across AWS, Azure, and other clouds using infrastructure as code. Integrate and operate LLM systems, configure AI coding agents, develop expertise in areas such as retrieval, agent orchestration, guardrails, and MCP, and guide enterprise adoption through coaching, presentations, architecture forums, and technical documentation.

Own AI product discovery, workflow and agent design, prototyping, acceptance testing, roadmaps, adoption, value measurement, and responsible AI controls. Translate client needs into buildable solutions while collaborating with engineers, consultants, risk, legal, security, and users. Build and refine agentic prototypes, define human-in-the-loop experiences, validate outcomes, and support deployment, training, and continuous improvement.

Owns architecture and technical standards for AI/ML platforms, agent frameworks, MCP integrations, orchestration, context engineering, evaluation, governance, and human approval controls. The role guides AI engineers, mentors technical teams, reviews complex designs, and partners with platform, data, network, and UI/UX engineering groups. Responsibilities include production readiness, secure development, monitoring, retrieval systems, tool safeguards, auditability, and enterprise adoption of emerging AI technologies.

Leads multiple engineering teams building agentic AI, machine learning, data science, and enterprise AI platforms. Oversees architecture, production delivery, scalability, security, governance, CI/CD, observability, and model lifecycle management. Partners with product, cloud, security, and infrastructure teams to define roadmaps and deliver reusable AI frameworks using LLMs, RAG, vector databases, agent orchestration, and microservices. Mentors engineering leaders and communicates execution, risks, and outcomes to senior stakeholders.

Build agentic AI platform runtimes and production agents, including planning loops, tool calling, durable execution, retrieval, human approvals, evaluation harnesses, guardrails, observability, and audit trails. Integrate enterprise systems and operate backend services on AWS while improving reliability, security, cost, and traceability of non-deterministic multi-step systems.