enterprise ai engineer

Blackberg Group

Blackberg is seeking a Data, AI &amp; DevSecOps Engineer to support enterprise AI integration, data analytics, secure development, and system performance optimization for one of the national institutes under Health and Human Services (HHS). This role ensures that government systems leverage advanced technologies while maintaining security, compliance, and operational reliability, enabling data-driven decision-making and continuous improvement.Key Responsibilities:<ul><li>Configure and support enterprise AI platforms (e.g., Microsoft Copilot, CHIRP, Assure OpenAI)</li><li>Develop and maintain Qlik Sense dashboards and analytics solutions</li><li>Perform database administration and development (SQL/Azure SQL, data integration, optimization)</li><li>Implement DevSecOps practices, including CI/CD pipelines and automated testing</li><li>Conduct security testing (SAST/DAST), vulnerability remediation, and compliance validation</li><li>Support API integrations and enterprise data exchange mechanisms</li><li>Monitor system performance and analytics to support continuous improvement</li><li>Contribute to system design documentation, testing artifacts, and technical reporting</li></ul>Required Qualifications:<ul><li>Experience supporting enterprise AI platforms and emerging technologies</li><li>Strong background in data analytics, BI tools (Qlik Sense), and database management</li><li>Experience with DevSecOps tools, secure coding practices, and Federal security frameworks</li><li>Knowledge of API integrations, cloud platforms, and enterprise data architectures</li><li>Experience supporting complex system integrations in regulated environments</li></ul>Preferred Qualifications:<ul><li>Experience with NIH/HHS systems, data environments, or security requirements</li><li>Certifications in cloud platforms, security (e.g., CISSP), or data analytics</li><li>Familiarity with SonarQube, automated testing tools, and CI/CD pipelines</li><li>Experience supporting AI governance, model validation, or responsible AI practices</li></ul>Job Type: Full-timePay: $130,000.00 - $170,000.00 per yearBenefits:<ul><li>401(k)</li><li>401(k) matching</li><li>Dental insurance</li><li>Employee assistance program</li><li>Employee discount</li><li>Flexible spending account</li><li>Health insurance</li><li>Paid time off</li><li>Parental leave</li><li>Professional development assistance</li><li>Vision insurance</li></ul>Application Question(s):<ul><li>Describe your experience configuring or supporting enterprise AI platforms (e.g., Microsoft Copilot, OpenAI, AI Builder).</li><li>What is your experience developing Qlik Sense dashboards or similar BI tools?</li><li>Describe your experience with database administration (SQL/Azure SQL) and performance optimization.</li><li>Do you have any experience supporting the federal government, particularly Health and Human Services?</li></ul>Work Location: Remote

Data, AI, and DevSecOps Engineer (BI / Security / Integration Specialist)

HELO Solutions

Location: Remote (Global)Compensation: Competitive, dependent on experienceType: Full-Time Contractor (2+ months, potential to extend)Reports to: Managing PartnerAbout HELO SolutionsHELO Solutions is an AI consulting and product development company. We build production AI systems &mdash; agents, automation platforms, and intelligent workflows &mdash; for enterprise clients across industries including CPG, financial services, legal, and music. We also develop our own AI products. We are a small, high-output team that ships fast, operates with minimal bureaucracy, and holds a high bar for work that actually runs in production and gets used.About the RoleWe are hiring an AI Engineer who has built agents that are live in production at real companies &mdash; not demos, not prototypes that died after a pitch, not research projects. We need someone who has shipped agentic systems that enterprises depend on every day, and who understands the full lifecycle: scoping the business problem, designing the agent architecture, integrating with messy enterprise systems, building evaluation and monitoring, and keeping it running reliably.This role sits across two priorities equally: building our internal multi-agent AI platform and delivering agent-based solutions directly to enterprise clients. You will work closely with our engineering team and the Managing Partner. You will have significant autonomy and direct impact on both the product and the business.Responsibilities<ul><li>Design, build, and deploy AI agents and multi-agent systems that solve real business problems for enterprise clients.</li><li>Architect and implement agent orchestration &mdash; coordination, handoffs, shared state, and communication between specialized agents within our multi-agent platform.</li><li>Integrate agents with enterprise systems (ERPs, CRMs, accounting platforms, APIs) to execute workflows end-to-end in production environments.</li><li>Build evaluation frameworks and production monitoring for deployed agents &mdash; logging agent decisions, tracking accuracy, cost, and latency, and implementing feedback loops.</li><li>Work within client systems to understand existing workflows and translate messy business processes into agent-executable architectures.</li><li>Identify and codify repeatable deployment patterns across engagements to accelerate future delivery.</li><li>Maintain deep, current knowledge of LLM capabilities, agent frameworks, implementation patterns, and the evolving AI product development stack.</li><li>Deliver technical artifacts including agent skills, sub-agents, API integrations, and supporting infrastructure that will be used in production workflows.</li><li>Contribute to internal product development, shaping the architecture and capabilities of our platform alongside the engineering team.</li></ul>Requirements<ul><li>Production agent experience &mdash; You have built AI agents that are currently running in production for real companies. You can point to systems you built that people use every day.</li><li>Python proficiency &mdash; Strong programming skills with Python as your primary language. You ship clean, maintainable production code.</li><li>LLM fluency &mdash; Deep working knowledge of prompt engineering, context window management, and model selection. You know when to use a smaller model vs. a frontier model and why.</li><li>Enterprise integration experience &mdash; You have connected agents to real business systems: ERPs (Acumatica, NetSuite, SAP), CRMs (Salesforce, HubSpot), accounting platforms (QuickBooks, Xero), or comparable enterprise APIs.</li><li>Evaluation and monitoring in production &mdash; You have built observability into agents. You know how to log agent decisions, track accuracy and cost, and build feedback loops that improve agent performance over time.</li><li>Multi-agent orchestration &mdash; You have designed or built systems where multiple specialized agents coordinate, hand off tasks, and share state to accomplish complex workflows.</li><li>High agency &mdash; You can take a messy business problem and turn it into a working agent architecture without hand-holding. You thrive in ambiguity and move fast with minimal direction.</li><li>Portfolio / case study &mdash; You must be able to walk us through at least one agent system you built that is live and being used by a real company. We want to understand the business problem, your architecture decisions, and how it is performing.</li></ul>Nice-to-Haves<ul><li>TypeScript, Java, or a strong second language beyond Python.</li><li>Customer-facing or consulting experience &mdash; you have worked directly with clients or stakeholders to scope and deliver technical projects.</li><li>Domain experience in financial services, CPG, legal, or the music industry.</li><li>Cost optimization thinking &mdash; token management, caching strategies, model routing to keep agent operating costs viable for clients.</li><li>Experience with workflow orchestration tools (Temporal, Airflow, Step Functions, or similar).</li><li>Former technical founder or early-stage startup experience &mdash; you have worn many hats and shipped under resource constraints.</li></ul>How to ApplySend us your resume and a brief write-up of an agent system you built that is live in production. We want to know: What was the business problem? What was your architecture? How is it performing? If multiple people contributed, tell us which part was yours. Keep it concise &mdash; a few paragraphs is plenty.What We Offer<ul><li>Remote-first, global team &mdash; work from anywhere.</li><li>Direct impact &mdash; you will shape both our products and our client engagements from day one.</li><li>Small team, no bureaucracy &mdash; decisions happen fast, your work ships fast.</li><li>Exposure to diverse enterprise AI challenges across multiple industries and verticals.</li><li>Competitive compensation dependent on experience.</li></ul>HELO Solutions LLC is a Delaware-based company. We are an equal opportunity employer committed to building a diverse team.Pay: $105,000.00 - $140,000.00 per yearApplication Question(s):<ul><li>Name one AI agent you built that is currently running in production at a real company. What does it do?</li><li>You need to deploy 3 agents that each call different enterprise APIs. One API is flaky and times out 20% of the time. How do you handle this in production?</li><li>When would you use Claude Haiku instead of GPT-4o for an agent task, and why?</li></ul>Work Location: Remote

AI Engineer — Enterprise Agents

Stanford University

<div>Job Purpose 
Are you an AI/GenAI engineer who loves shipping real systems? Join Stanford&rsquo;s Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you&rsquo;ll work hands-on to implement LLM/RAG services, integrate with enterprise platforms (ServiceNow, Salesforce, Oracle Financials, etc.), and follow strong MLOps/SDLC practices. You&rsquo;ll prototype, harden, and ship features&mdash;partnering closely with product, security, infrastructure, and application teams. 
Please note: This is a one-year, fixed-term position. 
This is an applied engineering role (not research). You&rsquo;ll learn rapidly, contribute code daily, write clear docs, and develop strong habits in quality, governance, and cost/latency optimization. 
Core Duties<ul><li>
AI/ML System Implementation &amp; Integration: Assess user needs and requirements, 
</li><li>
Turn requirements and tickets into well-engineered components (data prep, pipelines, vector stores, prompts/agents, evaluation hooks). 
</li><li>
Application &amp; Agent Development: Build, maintain, and update programs like LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. 
</li><li>
RAG &amp; Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure&mdash;escalating architecture changes to designated architects. 
</li><li>
MLOps &amp; SDLC Practices: Contribute tests, CI/CD pipelines, telemetry, and prompt/model versioning; participate in code reviews and release activities across dev/test/prod; follow team software development methodology. 
</li><li>
Governance, Security &amp; Compliance: Apply established guardrails (PII redaction, policy checks, access controls/minimum-privilege). Document decisions and known risks. 
</li><li>
Metrics &amp; Reporting: Create programs to meet reporting and analysis needs; instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. (Deep BI/reporting not primary. 
</li><li>
Documentation &amp; Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities, user stories, and acceptance criteria; design and implement user and operations training programs; document changes in software for end users. Support and sometimes lead UAT/test activities. 
</li><li>
Collaboration &amp; Mentorship: Participate in working sessions with stakeholders; receive and give code review feedback; pair program with senior engineers; proactively upskill on platforms and frameworks.</li></ul> 
<div>Education &amp; Experience: 
Bachelor's degree and three years of relevant experience or a combination of education and relevant experience. 
Required Knowledge, Skills, and Abilities<ul><li>
Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post‑deployment support. 
</li><li>
Proven Delivery: Implemented 1+ AI/ML projects and 1+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. 
</li><li>
Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. 
</li><li>
Programming Expertise: Proficient in Python; familiarity with Node.js/TypeScript/React and RESTful APIs; ability to read/extend existing codebases. 
</li><li>
Vector &amp; Search Basics: Worked with at least one vector/search tech (e.g., Pinecone, OpenSearch/Elasticsearch, FAISS, Milvus) and basic embedding workflows. 
</li><li>
Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). 
</li><li>
Thorough understanding of SDLC, MLOps, and quality control practices. 
</li><li>
Ability to define/solve logical &amp; technical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. 
</li><li>
Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources.</li></ul> 
Desired Knowledge, Skills, and Abilities<ul><li>
MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights &amp; Biases. 
</li><li>
Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. 
</li><li>
Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. 
</li><li>
GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, CrewAI/AutoGen. 
</li><li>
Security &amp; Governance: Implementing AI guardrails, red-teaming, policy enforcement frameworks. 
</li><li>
Enterprise Integrations: ServiceNow ,Salesforce, Oracle Financials or others. 
</li><li>
UI Development: React/Next.js/Tailwind for internal tools. 
</li><li>
Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline &amp; online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). 
</li><li>
Parameter‑efficient fine‑tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open‑weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. 
</li><li>
Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. 
</li><li>
Hybrid search &amp; reranking (BM25+dense, Cohere/Voyage/Jina rerankers), synthetic data generation, provenance/watermarking. 
</li><li>
Telemetry &amp; governance: prompt/model drift monitoring, policy‑as‑code, audit logging, red‑teaming playbooks.</li></ul> 
Certifications and Licenses<ul><li>
Nice to have (not required): Google/AWS/Azure AI/ML certifications or a demonstrable portfolio (GitHub, write-ups, demos) of applied AI work.</li></ul> 
Physical Requirements*:<ul><li>
Constantly perform desk-based computer tasks. 
</li><li>
Frequently sit, grasp lightly/fine manipulation. 
</li><li>
Occasionally stand/walk, writing by hand. 
</li><li>
Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds.</li><li>Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.</li></ul> 
Working Conditions:<ul><li>
May work extended hours, evenings, and weekends.</li></ul> 
Work Standards:<ul><li>
Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. 
</li><li>
Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. 
</li><li>
Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, http://adminguide.stanford.edu.</li></ul> 
The expected pay range for this position is $113,148 to $137,516 per annum. 
Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location, and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. 
At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford&rsquo;s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. 
Why Stanford is for You: 
Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with:<ul><li>
Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak. 
</li><li>
A caring culture. We provide superb retirement plans, generous time-off, and family care resources. 
</li><li>
A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits. 
</li><li>
Discovery and fun. Stroll through historic sculptures, trails, and museums. 
</li><li>
Enviable resources. Enjoy free commuter programs, ridesharing incentives, discounts, and more. 
</li><li>
Redwood City. Our new Stanford Redwood City campus, opened in 2019, will be the workplace for approximately 2,700 staff, including University IT, whose jobs are important to supporting the University&rsquo;s mission. The campus will offer amenities such as onsite cafes and a dining pavilion, a high-end fitness facility with an outdoor pool, and a childcare center for Stanford families.</li></ul> 
The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned. 
 
Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form. 
 
Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.</div></div>

Junior AI Applications Engineer

FlairsTech

<div>
 
 
 About the Role
 
 
 
 
 
 
 We are hiring an elite cohort of highly hands-on AI Engineers to embed directly within Valsoft's Edelweiss Software Group portfolio of vertical market B2B SaaS companies.
 
 
 We are looking for builders &mdash; engineers who operate as system orchestrators rather than traditional syntax typists. You will design, implement, and deploy secure, enterprise-grade AI systems that automate workflows, eliminate manual processes, rapidly modernize massive legacy codebases, and create measurable business impact.
 
 
 You will work directly with operators, product teams, and leadership to turn inefficiencies into intelligent systems. Operating with a distinct startup mindset within a large-scale corporate portfolio, your primary focus is driving rapid product innovation, executing AI-driven legacy modernization via agentic swarm coding, and maximizing ROI through autonomous multi-agent workflows.
 
 
 If you enjoy owning problems end-to-end, possess a relentless bias toward action, and thrive on shipping real systems into production at an unprecedented velocity, this role is for you.
 
 
 Your mission: act as an organizational force multiplier to increase efficiency, reduce CAC payback periods, increase Net Revenue Retention (NRR), and unlock massive scalability.
 
 
 What You&rsquo;ll Do
 
 
 Build &amp; Ship AI Systems
 
 <ul>
 <li>
 Design, develop, and deploy production-grade, secure AI systems utilizing scalable polyglot microservices.
 </li>
 <li>
 Integrate state-of-the-art enterprise LLMs (Anthropic Claude Opus 4.6, OpenAI GPT-5.2, Google Gemini 3) into SaaS platforms via dynamic, fault-tolerant API routing gateways.
 </li>
 <li>
 Modernize extensive codebases using agentic swarm coding, automated refactoring tools, and cross-language translation.
 </li>
 <li>
 Develop autonomous multi-agent workflows utilizing parallel orchestration tools like the OpenAI Codex and Claude Code CLI.
 </li>
 <li>
 Automate comprehensive technical documentation for legacy and newly generated codebases using AI orchestration.
 </li>
 <li>
 Rapidly prototype &rarr; validate via adversarial testing &rarr; deploy &rarr; iterate.
 </li>
 </ul>
 
 Architect AI Infrastructure
 
 <ul>
 <li>
 Apply good knowledge of software engineering design patterns and architecture to ensure the scalability and maintainability of AI-generated systems.
 </li>
 <li>
 Implement complex RAG systems and continually optimize trade-offs between massive-context hydration (up to 1-million tokens) and multi-stage semantic retrieval.
 </li>
 <li>
 Work extensively with vector databases (Pinecone, Weaviate, FAISS) and manage persistent document embedding queues.
 </li>
 <li>
 Build polyglot microservices specifically capable of handling massive, long-running operational tasks and streaming continuous token generation via persistent connection protocols (WebSockets and Server-Sent Events).
 </li>
 <li>
 Ensure SOC2 compliance, data residency (via inference_geo parameters), and deterministic execution through policy-as-code agentic governance.
 </li>
 <li>
 Deploy and scale models strictly within secure managed cloud boundaries (Azure AI, AWS Bedrock, GCP Vertex) leveraging Docker and Kubernetes orchestration.
 </li>
 </ul>
 
 Transform the Application Lifecycle &amp; Collaborate
 
 <ul>
 <li>
 Architect "zero-touch deployment" CI/CD pipelines fortified with adversarial gating.
 </li>
 <li>
 Design and implement unit test and benchmark automation workflows, utilizing AI agentic coding to rigorously validate and test produced code.
 </li>
 <li>
 Natively integrate synthetic red teaming into CI/CD to actively prevent prompt drift, reward hacking, and logic degradation.
 </li>
 <li>
 Work directly with non-technical stakeholders to translate ambiguous business problems into secure, scalable AI solutions.
 </li>
 <li>
 Optimize Atlassian workflows (Jira) to automate the translation of unstructured product requirements into structured prompt contexts for AI agents.
 </li>
 </ul>
 
 Required Technical Skills
 
 
 Core Engineering
 
 <ul>
 <li>
 5+ years of demonstrable enterprise software development experience.
 </li>
 <li>
 Good knowledge of software engineering design patterns and architecture.
 </li>
 <li>
 Flexible in most popular programming languages (Python, Javascript, Typescript, .Net, C#) and web frameworks (NextJS, etc.).
 </li>
 <li>
 Ability to switch environments and programming using agentic programming tools to validate and test your produced code.
 </li>
 <li>
 Backend development experience designing polyglot APIs, decoupled asynchronous microservices, and utilizing persistent connection protocols (WebSockets/SSE).
 </li>
 <li>
 Familiarity with containerization (Docker, Kubernetes) and architecting multi-region, fault-tolerant cloud infrastructure (AWS, Azure, or GCP).
 </li>
 </ul>
 
 AI &amp; ML Systems
 
 <ul>
 <li>
 Enterprise LLM integration and dynamic API routing (OpenAI, Anthropic, Gemini, DeepSeek).
 </li>
 <li>
 Multi-agent orchestration and advanced CLI tooling (Claude Code with Auto Hot-Reload and Forking Context, OpenAI Codex).
 </li>
 <li>
 Mastery of localized AI IDE layers (Cursor for repository-wide reasoning, GitHub Copilot).
 </li>
 <li>
 Massive-context RAG pipelines, dynamic chunking strategies, and vector databases (Pinecone, Weaviate, FAISS).
 </li>
 <li>
 Custom evaluations (LangSmith), structured outputs, and managed fine-tuning in secure cloud boundaries.
 </li>
 </ul>
 
 MLOps &amp; Process Automation
 
 <ul>
 <li>
 Zero-touch CI/CD pipelines and advanced Git workflows (including Git worktree isolation for autonomous sub-agents).
 </li>
 <li>
 Unit test and benchmark automation frameworks fully integrated with AI-driven testing.
 </li>
 <li>
 Adversarial LLM testing and automated synthetic red-teaming integration.
 </li>
 <li>
 Monitoring dynamic access limits and real-time atomic token/credit consumption tracking.
 </li>
 <li>
 Strict hallucination mitigation, enterprise guardrails, and deterministic policy-as-code execution.
 </li>
 </ul>
 
 Profile We&rsquo;re Looking For
 
 <ul>
 <li>
 Highly hands-on &mdash; you ship, not just design. You operate at an unprecedented velocity by orchestrating advanced autonomous tools.
 </li>
 <li>
 Entrepreneurial startup mindset &mdash; you see legacy inefficiencies and act with a relentless bias toward rapid action.
 </li>
 <li>
 Comfortable with ambiguity and translating complex business needs into precise technical execution.
 </li>
 <li>
 Business-oriented &mdash; you care deeply about ROI, operational leverage, and driving specific financial metrics (CAC, NRR).
 </li>
 <li>
 Fast learner &mdash; keeps up with real-time shifts in AI ecosystems, hardware architectures (e.g., Cerebras chips), and vital API migrations.
 </li>
 <li>
 Pragmatic &mdash; uses the right secure enterprise tool for the specific bottleneck, eschewing the fanciest public tool if it compromises stability or compliance.
 </li>
 </ul>
 
 
</div>

Senior AI Engineer

Apple

Imagine what you could do here. At Apple, revolutionary ideas have a way of becoming extraordinary products, services, and customer experiences. Join the Ai Data Platform Applied Machine Learning team to pioneer enterprise solutions where generative AI meets Apple's unique commitment to privacy-first innovation. Together, we'll create tools that redefine industries while safeguarding what matters most - our users' trust. 
 
Description 
 
As a pivotal member of Apple&rsquo;s enterprise generative AI efforts, you will help design, build, and evolve platforms and applications that power high-impact AI experiences across the company. You will contribute to the architecture and optimization of systems built for high availability, scalability, and reliability, working across backend services and application layers. 
 
Our team designs and implements scalable RESTful APIs, services, and platform components primarily using Python. We tackle unique challenges in privacy-preserving generation, efficient inference, and multimodal integration, while enabling teams to build on top of our foundations. We deliver production-grade systems and models that meet Apple&rsquo;s rigorous standards for quality, performance, and scalability. 
 
Preferred Qualifications 
 
MS in Computer Science, Machine Learning, or a related quantitative field 
 
Experience with microservices architecture and distributed systems 
 
Experience in ML frameworks (PyT orch, JAX) for training, fine-tuning, and deploying ML/generative models at scale 
 
Proven track record of building enterprise-grade ML pipelines (data prep, distributed training, optimization, monitoring) in cloud environments (AWS, GCP , Azure) or on-prem infrastructure 
 
Minimum Qualifications 
 
Bachelor of Science in Computer Science, Machine Learning, or a related quantitative field or equivalent experience 
 
2+ years of hands-on experience in machine learning and/or backend development in industry 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $126,800 and $220,900, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Machine Learning Engineer, (Applied Machine Learning), AI & Data Platforms (AiDP)

CVS Health

<div>We&rsquo;re building a world of health around every individual &mdash; shaping a more connected, convenient and compassionate health experience. At CVS Health&reg;, you&rsquo;ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger &ndash; helping to simplify health care one person, one family and one community at a time.
At CVS Health, our purpose is to deliver better health outcomes by meeting consumers where they are&mdash;through local care, digital experiences, and a nationwide team committed to quality, safety, and affordability.
Our Solutions Engineering and Infrastructure organization is building an enterprise AI/ML capability that delivers reliable, responsible, and secure AI-powered platforms and solutions at Fortune 5 scale, and this role is foundational to help us develop that capability.
This is a senior individual-contributor role, focused on identifying evaluating and documenting high-value use cases, designing and prototyping AI-powered solutions, and evolving them into secure, resilient, enterprise-ready products and platform components.
Key Responsibilities:
AI Solution Design &amp; Prototyping<ul><li>
Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics.</li><li>
Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations.</li><li>
Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations.</li></ul>
Production Engineering &amp; Enterprise Readiness<ul><li>
Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns.</li><li>
Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering).</li><li>
Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks.</li></ul>
Model Enablement &amp; Evaluation<ul><li>
Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments).</li><li>
Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency.</li><li>
Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services.</li></ul>
Reusable Components &amp; Engineering Excellence<ul><li>
Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement.</li><li>
Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices.</li><li>
Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.</li></ul>
Required Qualifications:<ul><li>
7+ years of software engineering supporting Data or AI/ML initiatives, including building and operating production services.</li><li>
3+ years applying ML/AI in production; demonstrated hands-on GenAI delivery (LLMs, RAG, evaluation, and safety controls)</li><li>
3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements.</li></ul>
Preferred Qualifications<ul><li>
Strong full-stack engineering skills (backend services, APIs, and modern web application development) with a focus on reliability and security.</li></ul><ul><li>
Hands-on expertise with LLM application patterns: RAG, tool/function calling, prompt management, evaluation, and guardrails.</li><li>
Experience with Python and at least one additional backend language; familiarity with common ML libraries and serving frameworks.</li><li>
Working knowledge of containerization and Kubernetes, CI/CD, infrastructure-as-code concepts, and production observability.</li><li>
Ability to communicate clearly, influence across teams, and translate business needs into implementable technical plans.</li></ul>
Education:<ul><li>
Bachelor&rsquo;s degree or equivalent experience (High School Diploma and 4 years relevant experience)</li></ul>
Pay Range
The typical pay range for this role is:
$106,605.00 - $284,280.00 
This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company&rsquo;s equity award program. 
Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.
Great benefits for great people
We take pride in our comprehensive and competitive mix of pay and benefits &ndash; investing in the physical, emotional and financial wellness of our colleagues and their families to help them be the healthiest they can be. In addition to our competitive wages, our great benefits include:<ul><li>
Affordable medical plan options, a 401(k) plan (including matching company contributions), and an employee stock purchase plan.</li><li>
No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching.</li><li>
Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility.</li></ul>
For more information, visit https://jobs.cvshealth.com/us/en/benefits
We anticipate the application window for this opening will close on: 03/26/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.</div>

Staff Machine Learning Engineer - Generative AI & Full-Stack Applications

Search 10000+ Enterprise ai engineer jobs near you this March. New roles added regularly across leading industries. Apply today.

10000 enterprise ai engineer jobs Near Me

Data, AI, and DevSecOps Engineer (BI / Security / Integration Specialist)

AI Engineer — Enterprise Agents

Junior AI Applications Engineer

Senior AI Engineer

Machine Learning Engineer, (Applied Machine Learning), AI & Data Platforms (AiDP)

Staff Machine Learning Engineer - Generative AI & Full-Stack Applications

Staff Machine Learning Engineer - Generative AI & Full-Stack Applications