This job has closed.

NorthITE · 2 days ago

Lead AI Engineer

Austin, Texas Metropolitan Area

Contract

Hybrid

Senior Level, Lead/Staff

$70/hr - $75/hr

5+ years exp

NorthITE is seeking a Lead AI Engineer to modernize and enhance their existing search systems. This role involves transforming traditional search into an intelligent, context-aware experience through the integration of semantic search and LLM-powered ranking techniques.

Information Technology & Services

Hiring Manager

Narresh Gujjeti

Responsibilities

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

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

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

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

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

Qualification

ElasticSearchPythonLLMAWSSemantic SearchGenAIRe-RankingOpenSearchSearch EngineerBM25APIsFastAPIFlaskDocker

Required

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