Senior Machine Learning Engineer

Tekmetric is a cloud-based software company helping automotive repair shops manage operations and grow. The Senior Machine Learning Engineer will build and operate production AI systems, including retrieval-augmented generation pipelines, semantic search, recommendation models, embedding infrastructure, and LLM integrations. The role also involves developing evaluation frameworks and collaborating with data and application engineers to integrate ML capabilities into product workflows.

Responsibilities:

• Design and build RAG pipelines — chunk store schema, Redshift-sourced population, and the signal-based retrieval layer that assembles context for LLM-powered suggestions in real time
• Own the semantic search infrastructure over operational data: embedding pipeline, chunking strategy, hybrid BM25+vector retrieval, and the reranking layer that feeds LLM tool calls (PGvector)
• Build a recommendation model end-to-end — training pipeline (feature extraction, model development, evaluation) and the SageMaker inference path that surfaces relevant suggestions at the right moment in the user workflow
• Design tool definitions, prompt assembly, and context selection for Bedrock-backed AI features, working directly with application engineers who wire these into the product
• Compute and serve ML features that recommendation models and customer analytics depend on: entity-level historical aggregates, account behavioral profiles, and engagement history
• Build and maintain batch and incremental embedding pipelines via Bedrock (Titan, Cohere) with CDC-based freshness, keeping the vector store current with the operational database
• Build evaluation frameworks — offline retrieval metrics, model quality tracking, and A/B infrastructure needed to measure whether AI features are driving business outcomes
• Collaborate with data engineers to integrate ML models into data pipelines and surface results through search and analytics APIs

Requirements:

• 4+ years in ML engineering with clear experience taking a model or ML feature from experimentation into production — not just research or notebooks
• Retrieval systems depth — embeddings, vector similarity, and ANN search at the mechanism level; ability to reason about BM25 vs. dense retrieval, chunking tradeoffs, and when reranking adds value (PGvector, FAISS, Qdrant, Weaviate, Pinecone)
• LLM application patterns — hands-on experience with RAG, tool use/function calling, prompt engineering, and context assembly; debugging retrieval pipelines where model output was wrong because the context was wrong (Bedrock, OpenAI, Anthropic SDK, LangChain)
• Strong Python and ML tooling — production-quality training and inference code, not just exploratory notebooks (PyTorch, scikit-learn, Hugging Face, XGBoost)
• Data engineering fundamentals — writing the SQL or Spark job that extracts training data without handing it off; understanding point-in-time correctness and feature leakage
• ML infrastructure on AWS — deploying and operating models in a real cloud environment (SageMaker, Bedrock, S3, Lambda)
• Workflow orchestration — building ML pipelines that run on a schedule, handle failure gracefully, and do not require manual intervention to stay healthy (Airflow, Prefect, or Kubeflow)
• Fine-tuning or PEFT — experience adapting embedding or generative models to a domain-specific corpus
• Graph databases — Apache AGE, Neo4j, or similar; understanding how graph traversal complements vector similarity for multi-hop relationship queries
• Streaming and CDC — Kinesis, Kafka, or Debezium; keeping a vector store or feature store current with a live operational database
• Recommendation systems — collaborative filtering, two-tower models, or contextual bandits in a production setting
• Domain experience in operational data — automotive, field service, logistics, healthcare; any domain where records are sparse, the vocabulary is specialized, and keyword search reliably fails