Top Artificial Intelligence Jobs

Build and productionize LLM-powered applications, agent workflows, and RAG pipelines for legal workflows. Engage customers to define problems, prototype solutions, and enable engineering teams with reusable AI patterns, observability, and tooling. Collaborate cross-functionally to deploy cloud-native AI systems and improve developer adoption.

Own product areas within HPC/AI system management and orchestration software: define requirements, prioritize features, create PRDs and user stories, collaborate with architects and engineering, engage customers, support releases and go-to-market, and monitor product performance to drive improvements.

Own the technical design and operation of an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, and ChatGPT. Build production AI agents, workflows, integrations, APIs, connectors, and RAG solutions using MCP and function calling. Ensure security, permissions, governance, reliability, evaluation, and observability. Administer platform configurations and troubleshoot complex issues. Establish development and deployment standards, lead technical reviews, mentor contributors, evaluate emerging capabilities, and serve as the senior technical escalation point.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and user trust. Provide rigorous, opinionated feedback distinguishing response quality levels and help define best practices for tools such as OpenAI Codex, Claude Code, and Cursor. This contract role requires strong software engineering expertise, familiarity with AI-assisted development workflows, and the ability to assess code and explanations without deeply reviewing or executing every line.

Owns the technical design and operation of an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, ChatGPT, and related systems. Builds production AI agents, workflows, integrations, APIs, connectors, and RAG solutions using MCP and function calling. Ensures security, permissions, governance, reliability, evaluation, and observability. Administers platforms, troubleshoots complex issues, establishes engineering standards, leads technical reviews, mentors contributors, and advises leadership on emerging AI capabilities.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Create daily 60–90-second vertical micro-drama episodes for TikTok, Instagram Reels, and YouTube Shorts using AI video, image, and audio tools. Generate scenes, edit footage in 9:16 format, maintain character consistency, synchronize music and sound effects, add captions and overlays, and optimize exports for social platforms. The role requires prompt engineering, visual composition, and editing skills, with traditional production and storytelling experience considered advantageous.

Designs, optimizes, and deploys machine learning models for mobile, embedded, and other resource-constrained edge devices. Responsibilities include quantization, pruning, distillation, hardware-aware optimization, inference runtime development, performance profiling, secure model deployment, telemetry, edge-cloud architecture, and responsible AI evaluation. The engineer collaborates with hardware, firmware, and product teams while maintaining technical documentation and evaluating emerging edge AI technologies.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Build and improve an internal AI agent platform using LLM APIs, tool calling, structured outputs, SQL, Python execution, and advertising APIs. Manage context, memory, guardrails, reliability, and evaluation frameworks while expanding document and workflow automation. Collaborate with machine learning, product, and full-stack teams to deliver robust AI applications and productivity tools.

Build and operate large-scale data infrastructure for AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery across diverse modalities. The role requires operating petabyte-scale systems, applying distributed systems and data modeling expertise, supporting accelerator-based training, and following strong software engineering practices such as testing, CI/CD, and code review.

Principal Software Engineer serving as a technical authority for backend, data, and AI platform architecture. Responsibilities include designing large-scale distributed data platforms, streaming systems, APIs, data models, and production AI systems using LLMs, RAG, vector search, and agent orchestration. The role establishes architectural standards, governance, observability, security, and cost controls; contributes high-impact production code; guides modernization; partners with product and engineering leaders; and mentors senior engineers.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Apply model compression, quantization, pruning, hardware-aware optimization, performance profiling, and systems engineering to deliver reliable production AI outside the data center. Collaborate cross-functionally while addressing on-device privacy, security, compute, memory, energy, and connectivity constraints.

Designs, deploys, and operates secure cloud and hybrid infrastructure for enterprise AI workloads. Responsibilities include container orchestration, GPU-enabled compute, APIs, microservices, CI/CD, infrastructure-as-code, MLOps, model serving, monitoring, data platform integration, and security governance. The role partners with architecture, cybersecurity, data engineering, and application teams to enable AI solutions across corporate and manufacturing systems.

Own and engineer an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, and ChatGPT. Build production AI agents, RAG integrations, APIs, connectors, workflows, and secure enterprise integrations using MCP and function calling. Administer platform configurations, access controls, monitoring, testing, governance, and lifecycle management. Lead architecture reviews, troubleshoot complex platform issues, establish engineering standards, mentor contributors, and evaluate emerging AI capabilities.

Evaluate AI coding agents such as OpenAI Codex and Claude Code for usefulness, correctness, engineering judgment, reasoning quality, and user trust. Provide rigorous, opinionated feedback, distinguish response-quality levels, and help define best practices for AI-assisted development interactions. The role requires senior engineering judgment rather than production coding, with a take-home evaluation and behavioral interview.

Optimize throughput, latency, and cost across large-scale neural network training and inference workloads. Responsibilities include GPU kernel optimization, distributed training and inference tuning, memory and parallelism optimization, compiler-level improvements, profiling, measurement, debugging, model compression, and production-scale LLM performance engineering. The role also involves cross-functional collaboration, code and design reviews, technical mentorship, and shipping reliable production systems.

Own and engineer an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, ChatGPT, and related systems. Build production AI agents, integrations, APIs, connectors, workflows, and RAG solutions while enforcing security, permissions, governance, and reliability. Administer platform configurations, troubleshoot complex issues, evaluate emerging capabilities, establish engineering standards, lead technical reviews, mentor contributors, and provide senior technical guidance.

Research and develop advanced machine learning systems, evaluate current AI techniques, and transition prototypes into scalable, observable production pipelines. Responsibilities include training, fine-tuning, and evaluating deep learning models; applying distributed and mixed-precision training; collaborating across the full ML lifecycle; and shipping impactful AI solutions. The role also involves assessing research literature, communicating complex methods, and continuously improving deployed systems with appropriate safeguards.

Leads enterprise AI transformation by developing the AI roadmap, prioritizing investments, managing initiative portfolios, and delivering measurable business value. Partners with executives and cross-functional teams to redesign operations, establish governance, evaluate technologies and vendors, drive adoption, and lead workforce change. Accountabilities include revenue growth, productivity, efficiency, customer experience, compliance, AI maturity, and value realization.

The Conversion Engineering-Global Content Consultant role focuses on consulting Global Data Conversion Software Engineering, improving processes, and implementing AI solutions. Responsibilities include supporting offshore teams, driving project completion, and creating documentation for standards.

Lead design, experimentation, and validation of agentic AI capabilities for legal products using ML, NLP, LLMs, RAG, and hybrid retrieval. Define agentic workflows, retrieval strategies, evaluation metrics, and benchmarking. Analyze large legal datasets, translate results into product recommendations, collaborate with product/engineering/legal experts, and mentor the data science team.

Own and grow the Accenture alliance in North America by creating joint value propositions and driving software ARR and CoE adoption. Manage end-to-end sales engagements, partner business planning, co-marketing, and pipeline alignment while collaborating with product, pre-sales, delivery, and Accenture teams. Build executive relationships, sell complex/multimillion-dollar solutions, and productize repeatable IP. Maintain deep product and market knowledge and travel as needed.

Own the maintenance, security hardening, multi-tenant provisioning, deployment, and operation of an AWS-hosted AI SaaS platform. The role will also help commercialize the product through packaging, positioning, and go-to-market efforts. Applicants should have independently shipped and operated a real SaaS product and be comfortable working as the sole or near-sole engineer. Compensation is currently revenue share, equity, or another negotiated arrangement without guaranteed cash pay.

Lead design and scale-up of robust downstream purification processes for biologics across development stages. Drive process characterization, regulatory filings, and technology innovation (continuous biomanufacturing, PAT). Collaborate cross-functionally, publish and present results, mentor staff, and influence strategic initiatives while championing safety and compliance.