Jobs

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

GenAI / Agentic AI Engineer

Wilmington, DE - OPEN TO RELOCATION CANDIDATES

Hybrid: 3 days onsite
12-month contract (extension possible)
$68-70/hr W2 – NO CTC NO THIRD PARTY

Must be currently authorized to work in the U.S.

We are able to support H-1B transfer candidates, but are
unable to consider OPT/CPT candidates

Required Skills

3+ years experience in Generative AI / LLM / Agentic AI
Strong Python (must-have): APIs, async, microservices, distributed systems
Experience building RAG pipelines, embeddings, vector search, LLM orchestration
Experience with FastAPI / Flask
Hands-on GCP + Kubernetes/OpenShift
CI/CD + DevOps tooling experience
Experience working in enterprise or regulated environments
Ability to work independently and drive AI solutions
LangChain / LlamaIndex – nice to have -NOT REQUIRED

Day to Day

Build Generative AI and Agentic AI systems, including LLM-based autonomous agents and multi-agent workflows
Design and implement RAG pipelines (embeddings, vector search, retrieval, grounding, prompt engineering)
Develop LLM orchestration systems (function calling, tool use, guardrails, evaluation, safety controls)
Build Python-based AI microservices and APIs using FastAPI / Flask
Work on distributed AI systems supporting high-scale inference and retrieval workloads
Develop and manage MCP (Model Context Protocol) servers and context frameworks
Deploy AI solutions on GCP (Vertex AI, GKE, Cloud Run) and OpenShift/Kubernetes
Build and maintain CI/CD pipelines for AI services in enterprise environments
Work with vector databases (Pinecone, Redis, pgVector, MongoDB Atlas Vector Search, etc.)
Ensure security, governance, compliance, and responsible AI standards (banking environment)

A company is looking for a Senior ML / LLM Engineer to design and implement advanced AI systems for clinical applications.

Key Responsibilities

Design and implement AI Agents and build RAG pipelines based on clinical guidelines

Develop systems to synthesize clinical data into prioritized insights for clinicians

Ensure compliance with HIPAA and implement vector search infrastructure

Required Qualifications

Bachelor's degree preferred

5 years of experience in ML / LLM engineering with production-grade systems

Proficiency in vector databases and MLOps tools (Docker, Kubernetes)

Experience with C# / .NET for backend development

Familiarity with healthcare interoperability standards (FHIR, HL7) is a plus

A company is looking for a Forward Deployed AI Engineer, Customer Adoption and Innovation. Key Responsibilities Lead the technical delivery for high-profile customer Proofs of Concept (POCs) and production pilots Provide expert advice on inference sizing, configuration, and resource management for OpenShift environments Enable field teams by creating reusable assets from customer engagements, including reference architectures and field manuals Required Qualifications 10+ years of experience in technical consulting, professional services, or solutions architecture 5+ years of hands-on experience with OpenShift or Kubernetes Familiarity with inference technologies for at least 2 years Functional skills in Python for automation and library interaction Strong consulting mindset with deep technical expertise in AI inference solutions

Staff Software Engineer - AI Orchestration 💰 Salary: $188,000 - 275,000 per year

At CoreWeave we are looking for a ML, AI engineer!

🛠️ Our tech stack:
AI, Cloud, Support, Kubernetes, Machine-Learning

📝 Rquirements:
- Over 8 years of experience in software engineering, particularly in distributed systems or cloud platforms
- Strong knowledge of Go programming language and experience in creating large-scale, long-term production systems
- In-depth understanding of Kubernetes internals, scheduling strategies, and controller-based architectures
- Proven experience in designing or enhancing orchestration, scheduling, or resource-management platforms
- Ability to lead technical projects across teams without direct authority
- Strong operational mindset with a background in managing mission-critical systems at scale
- Preferred experience with orchestration frameworks such as Kueue, Volcano, Ray, or similar Kubernetes-native tools
- Background in AI infrastructure, ML platforms, HPC, or extensive batch and streaming systems
- Comprehensive understanding of scheduling principles including fairness, pre-emption, quota management, and multi-tenant isolation
- Experience in defining and managing SLOs, capacity models, and significant reliability enhancements
- Contributions to open-source infrastructure or orchestration initiatives

👩‍💻👨‍💻 Your responsibilities are:
- Drive the technical vision and architecture for key aspects of the AI Workload Orchestration Platform
- Create scalable and reliable orchestration components for AI workloads across diverse schedulers and environments
- Lead cross-functional architectural reviews and ensure alignment among infrastructure, CKS, and managed inference teams
- Establish platform standards for reliability, observability, capacity management, and operational excellence
- Identify and address systemic performance, scalability, and fairness challenges across large GPU clusters
- Mentor senior engineers and foster technical leadership

Key Responsibilities Develop and maintain backend services using Core Java and Spring Boot. Integrate AI/LLM workflows into Java applications using frameworks like Spring AI. Design and consume RESTful APIs; familiarity with SOAP/WSDL/XML is a plus. Build and support event-driven systems using Kafka, JMS, and other messaging platforms. Collaborate on frontend development using frameworks such as React, Angular, or Vue. Implement observability tools for logging, metrics, and tracing. Contribute to CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes). Apply clean architecture principles and design patterns in system design. Participate in technical discussions, mentor team members, and collaborate across teams. Skill Metrics Restful APIs Docker Kubernetes Ai/LLM Java Spring Boot Required Skills 7–10+ years of experience in Java development and Spring ecosystem. Experience with AI/LLM integration in Java applications. Strong knowledge of REST APIs and event-driven architecture. Familiarity with frontend frameworks and responsive design. Experience with observability tools (Logback, Prometheus, OpenTelemetry). Proficiency in CI/CD, Docker, Kubernetes, and version control systems. Solid understanding of software design principles and architecture. Preferred Qualifications Experience with Spring Cloud and microservices components. Familiarity with vector databases (Pinecone, Milvus, Redis vector). Knowledge of domain-driven design, CQRS, and reactive programming (Spring WebFlux). Understanding of OAuth, JWT, and enterprise security protocols. Exposure to cloud-native AI deployments (AWS Lambda, Azure AI, Google Vertex AI). Experience with legacy system modernization and hybrid deployments. Knowledge of AI orchestration, prompt chaining, and multi-agent workflows. Experience with feature toggles, A/B testing, and canary deployments. Seniority level Mid-Senior level Employment type Contract Job function Engineering and Information Technology Industr

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Atika Tech, is seeking the following. Apply via Dice today!

Position: Agentic AI Engineer

Location: Chicago, IL

Duration: 12+ Months

Key Responsibilities

Architecture & Design: Lead the design and implementation of stateful, multi-agent systems using frameworks like LangGraph, CrewAI, or AutoGen.

Agent Orchestration: Develop robust reasoning loops (e.g., ReAct, Chain-of-Thought) and manage complex state, memory, and task hand-offs between specialized agents.

System Integration: Build secure interfaces (e.g., using Model Context Protocol (MCP)) to connect agents with enterprise APIs, databases, and external tools.

Production Excellence: Own the deployment, monitoring, and observability of agents in production using tools like Langfuse or LangSmith to trace reasoning and optimize performance.

Guardrails & Safety: Implement human-in-the-loop (HITL) mechanisms, input/output validation, and ethical guardrails to ensure agents operate safely and within regulatory boundaries.

Leadership: Mentor junior engineers, define coding standards for non-deterministic AI outputs, and drive the technical roadmap for agentic systems.

Required Technical Skills

Programming: Deep proficiency in Python and experience with backend frameworks like FastAPI.

AI Frameworks: Hands-on expertise in LangChain, LlamaIndex, and agent-specific orchestration patterns.

Data Systems: Strong knowledge of Vector Databases (e.g., Pinecone, Chroma), RAG architectures, and knowledge graphs.

Cloud & DevOps: Experience deploying AI workloads on AWS, Azure, or Google Cloud Platform using Docker and Kubernetes.

Evaluation: Proficiency in building automated evaluation harnesses (e.g., LLM-as-a-Judge) to measure accuracy and reduce hallucinations.

Qualifications

Experience: 5 8+ years in software engineering, with at least 2+ years focused on GenAI/Agentic AI implementations.

Education: Bachelor s

We Are

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are

An AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.

You'll shape how enterprises adopt AI-native engineering - either by leading complex agentic solutions and developing engineering talent, or by owning critical technical areas end-to-end as a senior IC

The Work

You’ll partner directly with client stakeholders — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.

Agent Architecture & Engineering
• Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.

AI Platform Integration
• Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
• Contribute to shared libraries, SDKs, and patterns that can be reused across clients.

Cloud-Native Engineering
• Leverage containerization (Kubernetes, Dock