Lead AI Engineer (Search Modernization) || Austin, TX, USA(Onsite)

Dice is the leading career destination for tech experts at every stage of their careers. Our client, E-Solutions, Inc., is seeking the following. Apply via Dice today! Job Title: Lead AI Engineer (Search Modernization) Job Location: Austin, TX, USA(Onsite) Job Type: Contract Job Description: Mandatory Skills: Elastic Search, OpenSearch, Python, LLM, GenAI, Semantic Search, Re-Ranking, AWS, Search Engineer We are looking for an AI Engineer to modernize and enhance our existing regex/keyword-based ElasticSearch 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 ElasticSearch 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. • 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. • 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. • 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. • 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. Thanks and Regards Ash Kumar Client Engagement Partner M: E: w: Lead AI Engineer (Search Modernization)1Machine Learning,Python,AWS,Data Science,AI,Data Scientist,ML,LLM,RAGN/AC2CUnited States