Lead AI Engineer Job Austin area,Texas USA,Software Development

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

Duration:
Longterm Contract

Lead AI Engineer (Search Modernization)

Mandatory

Skills:

Elastic Search, Open Search, 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 Elastic Search.
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 Elastic Search vectors, Open Search, 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:
- Open Search Service or EC2-managed Elastic Search
- Lambda, ECS/EKS, API Gateway, SQS/SNS
- Sage Maker 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 Elastic Search/Open Search
: 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 (Open Search, Sage Maker, 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.