Top 16 Llama text embedding Jobs

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Lead AI Engineer

Software Technology Inc -
Austin, TX

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Job Details

Contract

Qualifications

AI models
Containerization systems
AWS IAM
Search engines
Elasticsearch
Amazon Simple Notification Service (SNS)
Amazon ECS
Amazon SageMaker
Generative models
AI platforms (beyond public GPTs)
Machine learning cloud services
Model deployment
Implementing APIs
In-memory caching
S3
System deployment
Serverless cloud services
Amazon Simple Queue Service
Cloud solution engineering
Generative AI
AWS Lambda

Full Job Description

Job Title: Lead AI Engineer
Location: Austin, Texas (Hybrid)

Duration: Longterm Contract

Lead AI Engineer (Search Modernization)

Mandatory Skills: Elastic Search, OpenSearch, Python, LLM, GenAI, Semantic Search, Re-Ranking, AWS, Search Engineer

Job Description:
We are looking for an AI Engineer to modernize and enhance our existing regex/keyword-based Elastic Search system by integrating state-of-the-art semantic search, dense retrieval, and LLM-powered ranking techniques.
This role will drive the transformation of traditional search into an intelligent, context-aware, personalized, and high-precision search experience.

The ideal candidate has hands-on experience with Elastic Search internals, information retrieval (IR), embedding-based search, BM25, re-ranking, LLM-based retrieval pipelines, and AWS cloud deployment.

Roles & Responsibilities

Modernizing the Search Platform

Analyze limitations in current regex & keyword-only search implementation on ElasticSearch.
Enhance search relevance using:
BM25 tuning
Synonyms, analyzers, custom tokenizers
Boosting strategies and scoring optimization
Introduce semantic / vector-based search using dense embeddings.

2. LLM-Driven Search & RAG Integration

Implement LLM-powered search workflows including:
Query rewriting and expansion
Embedding generation (OpenAI, Cohere, Sentence Transformers, etc.)
Hybrid retrieval (BM25 + vector search)
Re-ranking using cross-encoders or LLM evaluators
Build RAG (Retrieval Augmented Generation) flows using ElasticSearch vectors, OpenSearch, or AWS-native tools.

3. Search Infrastructure Engineering

Build and optimize search APIs for latency, relevance, and throughput.
Design scalable pipelines for:
Indexing structured and unstructured text
Maintaining embedding stores
Real-time incremental updates
Implement caching, failover, and search monitoring dashboards.

4. AWS Cloud Delivery

Deploy and operate solutions on AWS, leveraging:
OpenSearch Service or EC2-managed ElasticSearch
Lambda, ECS/EKS, API Gateway, SQS/SNS
SageMaker for embedding generation or re-ranking models
Implement CI/CD for search models and pipelines.

5. Evaluation & Continuous Improvement

Develop search evaluation metrics (nDCG, MRR, precision@k, recall).
Conduct A/B experiments to measure improvements.
Tune ranking functions and hybrid search scoring.
Partner with product teams to refine search behaviors with real usage patterns.

Required Skills & Qualifications

5–10 years of experience in AI/ML, NLP, or IR systems, with hands-on search engineering.
Strong expertise in ElasticSearch/OpenSearch: analyzers, mappings, scoring, BM25, aggregations, vectors.
Experience with semantic search:
Embeddings (BERT, SBERT, Llama, GPT-based, Cohere)
Vector databases or ES vector fields
Approximate nearest neighbor (ANN) techniques
Working knowledge of LLM-based retrieval and RAG architectures.
Proficient in Python; familiarity with Java/Scala is a plus.
Hands-on AWS experience (OpenSearch, SageMaker, Lambda, ECS/EKS, EC2, S3, IAM).
Experience building and deploying APIs using FastAPI/Flask and containerizing with Docker.
Familiar with typical IR metrics and search evaluation frameworks.

Preferred Skills

Knowledge of cross-encoder and bi-encoder architectures for re-ranking.
Experience with query understanding, spell correction, autocorrect, and autocomplete features.
Exposure to LLMOps / MLOps in search use cases.
Understanding of multi-modal search (text + images) is a plus.
Experience with knowledge graphs or metadata-aware search.

Quick Apply

Refine Your Search

llama text embedding jobs

Lead AI Engineer

LLM Engineer - (Hybrid- Greenfield AI Project)

Associate Director - AI Engineering

Senior Data Scientist

Senior ML Engineer

Software Engineer IV - AI

Senior ML Engineer I

Sr AI/Agentic Engineer

Associate Principal/Forensic AI Engineer (Forensic Services practice)

Lead AI Architect, Salesforce Service Cloud & AWS

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