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,

Hi Professionals,

Title: Healthcare Senior NLP / LLM Engineer (MLOps & AWS)-Onsite Role,Need Local to TXExperience: 7+ Years
Location: Houston, TX (3 days/week onsite)

Mandatory Skills:

Strong expertise in NLP, Machine Learning, and Deep LearningProficiency in Python for ML/NLP solution developmentHands-on experience with LLMs, prompt engineering, and agentic workflows (e.g., LangGraph)Solid knowledge of data preprocessing, feature engineering, and data validationStrong MLOps skills: model versioning, CI/CD, pipelines, monitoring, retrainingExperience with Docker and KubernetesProficiency in SQL for data analysis and transformationExperience with AWS (EMR, Spark, PySpark, Bedrock)Knowledge of PostgreSQL or MySQLStrong understanding of testing, error analysis, and model validation "Disclaimer: E-Solutions Inc. provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws. We especially invite women, minorities, veterans, and individuals with disabilities to apply. EEO/AA/M/F/Vet/Disability.”

Healthcare Senior NLP / LLM Engineer (MLOps & AWS)-Onsite Role,Need Local to TX1AWS,NLP,health OR Healthcare,MLOps,LLMN/AC2CUnited States

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

Miracle Software Systems, looking for a Generative AI Engineer to build cutting-edge AI applications powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

If you’re passionate about building scalable AI systems that combine enterprise data with modern cloud and APIs, this role is for you.

Required Skills:
• 8+ years of software engineering experience
• Strong Python expertise
• Hands-on experience with LLMs / Generative AI
• Experience building RAG systems
• Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)
• Strong experience with FastAPI / REST APIs
• Solid knowledge of SQL & data processing
• Experience with GCP
• Familiarity with Git & Docker

What You’ll Do:
• Design and develop Generative AI applications using LLMs
• Build and optimize RAG pipelines using embeddings and vector databases
• Develop scalable REST APIs using FastAPI or similar frameworks
• Apply prompt engineering and model optimization techniques
• Build and manage data pipelines for AI systems
• Deploy solutions on Google Cloud Platform (GCP)
• Implement Docker-based deployments and CI/CD pipelines
• Collaborate with cross-functional teams to integrate AI into enterprise systems

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