LLM / GenAI Engineer

Evlo AI; Washington (District of Columbia); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Evlo AI is seeking a senior AI engineer to build production-grade LLM and generative AI systems, focusing on RAG pipelines, agentic workflows, and fine-tuning infrastructure. You will work on retrieval architectures, model optimization, and serving infrastructure with emphasis on latency, cost, and reliability.

This is hands-on engineering with impact across prototype to production. You will join a small, senior team that influences architecture decisions and ships features powering

Qualifications

• 3–8 years in software or ML engineering.
• At least 2 years of production experience with LLMs.
• Bachelor's degree in Computer Science, Engineering, or equivalent.

Responsabilités

• Design and ship production RAG systems with retrieval, chunking, and hybrid search.
• Build and tune vector database integrations and embedding pipelines.
• Develop agentic workflows and tool-use pipelines with error handling and guardrails.
• Implement systematic LLM evaluation, benchmarks, and regression testing.
• Run parameter-efficient fine-tuning and instruction tuning on domain datasets.
• Optimize inference cost and latency via quantization and batching.

Connaissances

Python

Async programming

API design

Cloud infra

RAG architectures

Vector databases

Embedding models

PEFT fine-tuning

LLM observability

Formation

Bachelor's degree in Computer Science, Engineering, or equivalent

Outils

LangChain

LlamaIndex

Pinecone

Weaviate

pgvector

Kubernetes

PyTorch

Triton

Description du poste

About The Role

The role focuses on building production-grade LLM and generative AI systems - RAG pipelines, agentic workflows, fine-tuning infrastructure, and evaluation frameworks that ship to real users. This is hands-on engineering, not prompt tinkering: the work spans retrieval architecture, model optimization, and serving infrastructure where latency, cost, and reliability all matter. The position sits on a small, senior AI team with direct influence over architecture decisions and a clear path from prototype to production. Models and pipelines built here power customer-facing product features at scale.

Key Responsibilities

• Design and ship production RAG systems - retrieval strategies, chunking, reranking, hybrid search - using LangChain, LlamaIndex, or custom architectures
• Build and tune vector database integrations (Pinecone, Weaviate, pgvector) and embedding pipelines for semantic search at scale
• Develop agentic workflows and tool-use pipelines with robust error handling, state management, and guardrails
• Implement systematic LLM evaluation: benchmark suites, LLM-as-judge pipelines, regression testing, and hallucination detection
• Run parameter-efficient fine-tuning (LoRA, QLoRA) and instruction tuning on domain-specific datasets; manage the full training-to-deployment loop
• Optimize inference cost and latency through quantization, batching, caching, and model routing across open-weight and frontier APIs
• Monitor production LLM systems for drift, cost anomalies, and quality regressions; contribute to team engineering standards and architecture reviews

What We Are Looking For

• 3-8 years of software engineering or ML engineering experience, including at least 2 years working with LLMs in production
• Strong Python skills; comfort with async programming, API design, and cloud infrastructure (AWS, GCP, or Azure)
• Hands-on experience with RAG architectures, vector databases, and embedding models in production environments
• Practical experience fine-tuning or adapting open-weight models (Llama, Mistral, Qwen) using PEFT methods
• Familiarity with LLM observability and evaluation tooling (LangSmith, Langfuse, Ragas, or homegrown equivalents)
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience; track record of shipping AI features end-to-end
• Bonus: Experience with Kubernetes-based model serving (vLLM, TGI, Triton), distributed training with PyTorch, or publishing/applied research in NLP