Top AI & Machine Learning Jobs

Build and ship generative AI applications for federal clients, including RAG pipelines, tool-using agents, APIs, and user interfaces. Develop with Python, TypeScript, Claude Code, AWS services, and OpenCORA; apply test-driven development, CI/CD, secure coding, observability, and LLM evaluation. Collaborate on requirements, Jira planning, pull requests, testing, releases, and client communication while maintaining traceability and delivering incremental production improvements.

Leads end-to-end delivery of generative AI applications for federal clients, serving as accountable architect, technical lead, and primary client contact. Designs AWS-based architectures using Bedrock, RAG, agentic workflows, and client integrations; manages agile delivery, backlogs, testing, infrastructure as code, security compliance, and production releases. Mentors engineers, reviews designs and code, gathers user feedback, improves delivery processes, and supports proposals and demonstrations.

Leads enterprise data and AI architecture across Snowflake, AWS, and Google Cloud. Designs governed data products, semantic layers, knowledge graphs, lakehouse platforms, integrations, and AI-ready foundations for analytics, machine learning, generative AI, and agentic applications. Establishes architecture standards for security, privacy, lineage, responsible AI, and interoperability; guides complex cross-functional initiatives; prototypes emerging technologies; influences senior stakeholders; and mentors architects and engineers.

Builds secure, reusable AI engineering platforms and agentic AI solutions in a Microsoft Azure environment. Responsibilities include designing agent workflows and integrations, developing CI/CD pipelines, implementing automated testing and security scanning, applying responsible AI and privacy controls, protecting sensitive data, monitoring performance and costs, and creating standards, playbooks, and training for enterprise AI adoption.

Conduct data analytics and quantitative research for investment strategies. Optimize data ingestion pipelines, create curated datasets, generate features and signals, analyze financial asset classes, and validate investment hypotheses through backtesting. Collaborate with engineering and portfolio management teams while developing expertise in financial markets and machine learning applications.

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.