AI Engineer (RAG & FineTuning Specialist)

Location: Hybrid/ Remote Type: Contract / FullTime Experience: 5+ Years Qualification: Bachelors or Masters in Computer Science or a related technical field Responsibilities: Architect & implement the RAG pipeline: embeddings ingestion, vector search (MongoDB Atlas or similar), and context-aware chat generation.Design and build Pythonbased services (FastAPI) for generating and updating embeddings.Host and apply LoRA/QLoRA adapters for peruser finetuning.Automate data pipelines to ingest daily user logs, chunk text, and upsert embeddings into the vector store.Develop Node.js/Express APIs that orchestrate embedding, retrieval, and LLM inference for realtime chat.Manage vector index lifecycle and similarity metrics (cosine/dotproduct).Deploy and optimize on AWS (Lambda, EC2, SageMaker), containerization (Docker), and monitoring for latency, costs, and error rates.Collaborate with frontend engineers to define API contracts and demo endpoints.Document architecture diagrams, API specifications, and runbooks for future team onboarding. Required Skills Strong Python expertise (FastAPI, async programming).Proficiency with Node.js and Express for API development.Experience with vector databases (MongoDB Atlas Vector Search, Pinecone, Weaviate) and similarity search.Familiarity with OpenAIs APIs (embeddings, chat completions).Handson with parametersefficient finetuning (LoRA, QLoRA, PEFT/Hugging Face).Knowledge of LLM hosting best practices on AWS (EC2, Lambda, SageMaker).Containerization skills (Docker): Good understanding of RAG architectures, prompt design, and memory management.Strong Git workflow and collaborative development practices (GitHub, CI/CD). NicetoHave: Experience with Llama family models or other opensource LLMs.Familiarity with MongoDB Atlas free tier and cluster management.Background in data engineering for streaming or batch processing.Knowledge of monitoring & observability tools (Prometheus, Grafana, CloudWatch).Frontend skills in React to prototype demo UIs. Location: Hybrid/ Remote Type: Contract / FullTime Experience: 5+ Years Qualification: Bachelors or Masters in Computer Science or a related technical field Responsibilities: Architect & implement the RAG pipeline: embeddings ingestion, vector search (MongoDB Atlas or similar), and context-aware chat generation.Design and build Pythonbased services (FastAPI) for generating and updating embeddings.Host and apply LoRA/QLoRA adapters for peruser finetuning.Automate data pipelines to ingest daily user logs, chunk text, and upsert embeddings into the vector store.Develop Node.js/Express APIs that orchestrate embedding, retrieval, and LLM inference for realtime chat.Manage vector index lifecycle and similarity metrics (cosine/dotproduct).Deploy and optimize on AWS (Lambda, EC2, SageMaker), containerization (Docker), and monitoring for latency, costs, and error rates.Collaborate with frontend engineers to define API contracts and demo endpoints.Document architecture diagrams, API specifications, and runbooks for future team onboarding. Required Skills Strong Python expertise (FastAPI, async programming).Proficiency with Node.js and Express for API development.Experience with vector databases (MongoDB Atlas Vector Search, Pinecone, Weaviate) and similarity search.Familiarity with OpenAIs APIs (embeddings, chat completions).Handson with parametersefficient finetuning (LoRA, QLoRA, PEFT/Hugging Face).Knowledge of LLM hosting best practices on AWS (EC2, Lambda, SageMaker).Containerization skills (Docker): Good understanding of RAG architectures, prompt design, and memory management.Strong Git workflow and collaborative development practices (GitHub, CI/CD). NicetoHave: Experience with Llama family models or other opensource LLMs.Familiarity with MongoDB Atlas free tier and cluster management.Background in data engineering for streaming or batch processing.Knowledge of monitoring & observability tools (Prometheus, Grafana, CloudWatch).Frontend skills in React to prototype demo UIs.