Lead AI Engineer

During the discovery stage, it will be 5 days working from office for the first 4 weeks of discovery

Duration: Longterm

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 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.

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.

Mohan Krishna Yarramsetti