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Llm Backend Engineer Jobs in Washington (NOW HIRING)

Trilagen

Agentic AI Engineer

Bethesda, MD · On-site

We need engineer's who can own the entire agent stack: a production frontend, a robust backend, a properly secured API and identity layer, a memory architecture that scales, and LLM integrations that ...

NSS

Full Stack Python Developer

Herndon, VA · On-site

$120 - $160/hr

Integrate backend services with Databricks/Spark pipelines and ML workflows to enable scalable ... Collaborate with ML Engineers to productionize LLM/RAG-based features, including prompt/inference ...

CYNET SYSTEMS

Senior Agentic AI Engineer

Reston, VA · On-site

$59 - $77/hr

Build LLM-powered applications for text generation, summarization, Q&A, conversational AI ... back-end technologies. * Design and implement single-agent and multi-agent systems for intelligent ...

Carlyle

Forward Deployed Product Engineer

Washington, DC · On-site

$210 - $220/hr

The Vice President owns a portfolio of products end-to-end--across backend services, modern frontends, data pipelines, and LLM-based workflows--and sets the technical direction, engineering standards ...

Bigbear.ai

AI Application Engineer

Mclean, VA · On-site

$120 - $180/hr

... combines LLM-powered chat (RAG), structured data (MCP/PostgreSQL), and dashboards to support ... You will own features end-to-end across backend APIs, database, and the modern React UI, with ...

Bespoketechinc

Software Engineer

Chantilly, VA · On-site

$90 - $120/hr

Develop and integrate LLM-powered capabilities, including Retrieval-Augmented Generation (RAG ... comparable modern backend frameworks). * Demonstrated hands‑on experience with and ...

InterImage

AI/ML Software Developer

Arlington, VA · On-site

... LLM/GenAI technologies into production environments. Key Responsibilities Full Stack Development ... Build scalable backend services using Python frameworks (e.g., Flask, FastAPI, Django) * Develop ...

InterImage

AI/ML Software Developer

Arlington, VA · On-site

... LLM/GenAI technologies into production environments. Key Responsibilities Full Stack Development ... Build scalable backend services using Python frameworks (e.g., Flask, FastAPI, Django) * Develop ...

InterImage

AI/ML Software Developer

Arlington, VA · On-site

... LLM/GenAI technologies into production environments. Key Responsibilities Full Stack Development ... Build scalable backend services using Python frameworks (e.g., Flask, FastAPI, Django) * Develop ...

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All JobsLlm Backend Engineer JobsLlm Backend Engineer Jobs in Washington

Llm Backend Engineer information

What is an LLM backend engineer?

LLM Backend Engineers are software engineers who specialize in designing, building, and optimizing the backend infrastructure that supports large language models (LLMs) like GPT-4. They focus on integrating LLMs into products and services, ensuring scalable APIs, managing data pipelines, and optimizing inference performance. Their work often involves deploying models in cloud environments, monitoring system reliability, and collaborating with AI researchers to bring advancements into production. LLM Backend Engineers play a critical role in making AI-powered applications robust, efficient, and accessible to end users.

What are the key skills and qualifications needed to thrive as an LLM backend engineer?

To thrive as an LLM Backend Engineer, you need a solid foundation in software engineering, backend architecture, and experience working with large language models, typically supported by a degree in computer science or a related field. Proficiency with programming languages like Python or Java, cloud platforms (AWS, GCP, Azure), and machine learning frameworks such as TensorFlow or PyTorch is essential, along with familiarity with APIs and containerization tools like Docker or Kubernetes. Strong problem-solving, collaboration, and communication skills distinguish top performers in this role. These skills ensure robust, scalable, and efficient deployment of LLM-powered applications while enabling effective teamwork and innovation.

What are some common challenges faced by LLM backend engineers when deploying large language models in production?

LLM Backend Engineers often encounter challenges such as optimizing inference latency, managing high resource consumption, and ensuring scalability for production workloads. Balancing model performance with cost efficiency requires careful selection of hardware, batching strategies, and model quantization techniques. Additionally, they must address security and privacy concerns associated with handling sensitive data processed by the models. Collaboration with data scientists and DevOps teams is essential to streamline model updates and monitor system health.

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Llm Backend Engineer jobs near you
Infographic showing various Llm Backend Engineer job openings in Washington as of August 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution.

Agentic AI Engineer

Trilagen

Bethesda, MD • On-site

Full-time

Medical, Dental, Retirement, PTO

Re-posted 28 days ago

Job description

We build AI agents that actually work in enterprise environments - not prototypes, not demos. We need engineer's who can own the entire agent stack: a production frontend, a robust backend, a properly secured API and identity layer, a memory architecture that scales, and LLM integrations that are model-agnostic and built to last.
You'll be deployed on client engagements as the lead technical architect and builder of agentic systems running in AWS, OCI, and Azure. You'll work directly with client stakeholders, translate complex requirements into working systems, and leave behind infrastructure clients can operate and extend. You'll also help Trilagen productize our delivery approach as we scale the practice.
If you've only ever built agents that run on your laptop, this isn't the role. If you've shipped agentic systems into production cloud environments and know exactly what breaks and why - we want to talk.
What you'll own
Full-stack agent development You design and build the entire application - not just the AI layer. This means a React or Next.js frontend with streaming, real-time agent UX; a Python or Node.js backend that orchestrates agent logic, manages state, and exposes clean APIs; and containerized, cloud-deployed services that operations teams can actually run. You own the repo, the CI/CD pipeline, the deployment, and the runbook.
Multi-cloud deployment Production agent systems on all three major clouds: AWS (Lambda, ECS/Fargate, Bedrock, API Gateway), Oracle OCI (OKE, Functions, AI Services), and Azure (AKS, Azure OpenAI Service, Azure Functions). You understand the tradeoffs between platforms and can advise clients on where to run what and why.
LLM integration and model strategy You have deep, hands-on experience with the leading LLM providers and their APIs - Anthropic Claude (Messages API, tool use, streaming, context management), OpenAI (GPT-4o, Assistants API, function calling), and Google Gemini (Gemini Pro/Flash, Vertex AI). You architect model-agnostic integration layers so clients aren't locked in, and you know how to select, swap, and benchmark models for specific agent tasks.
Agentic architecture You understand how to design systems that do real multi-step work: tool use and function calling patterns, ReAct and plan-and-execute loops, agent-to-agent orchestration and handoffs, human-in-the-loop checkpoints, retry and failure recovery strategies, and cost/latency optimization across long-running agent workflows. Frameworks like LangGraph, CrewAI, AutoGen, and the Model Context Protocol (MCP) are tools in your toolbox, not the ceiling of your knowledge.
Memory and context layer You've designed and implemented memory architectures for production agents: short-term conversational context, long-term persistent memory, RAG pipelines with vector databases (Pinecone, pgvector, OpenSearch, Weaviate), semantic search, and hybrid retrieval strategies. You know when to use each and how to keep them performant at scale.
Security and identity layer This is non-negotiable for our client base. You build the security envelope around every agent system you ship: OAuth 2.0 / OIDC authentication flows, API key lifecycle management, role-based access control enforced within agent workflows, secrets management (AWS Secrets Manager, Azure Key Vault, OCI Vault), audit logging for agent actions, prompt injection defense, and data residency controls. Familiarity with Okta or SailPoint ISC is a direct advantage on our engagements.
Requirements
  • 4+ years of software engineering experience with at least 2 years building and shipping LLM-powered or agentic applications in production cloud environments
  • Hands-on depth with at least two of the three major LLM providers: Anthropic Claude, OpenAI, and Google Gemini - at the API level, not just via wrappers
  • Full-stack proficiency: Python (FastAPI, Flask, or similar) backend, React or Next.js frontend, REST and WebSocket API design
  • Production experience on at least two of: AWS, Azure, OCI - with real deployments, not sandbox accounts
  • Demonstrated ability to design and implement agent memory and retrieval systems using vector databases and RAG
  • Strong command of AI security practices: auth, RBAC, secrets management, audit logging, and prompt-level safeguards
  • Consulting DNA - you can run a discovery session, write a technical design doc, manage client expectations, and own delivery end to end

Nice to have
  • Experience with all three LLM providers (Anthropic, OpenAI, Gemini) and model-agnostic orchestration patterns
  • Okta and/or SailPoint ISC integration experience
  • Cloud certifications: AWS Solutions Architect, Azure Solutions Architect, OCI Architect

Benefits
Benefits:
  • 401K
  • Health Insurance
  • Dental Insurance
  • Paid Time Off
  • Paid Sick Leave

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