What are the responsibilities and job description for the Lead AI Engineer position at Network Objects Inc.?
Job Details
Job Description:
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.