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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Tekmetric LLC - **Location:** Boston, MA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Apache HTTP Server, Automated Storage and Retrieval Systems, Encodings, Information Engineering, Software Debugging, Graph Database, Python (Programming Language), Machine Learning, Neo4j, Operational Data Store, Operational Databases, Recommender Systems, SQL Databases, Data Streaming, Workflow Management Systems, Pytorch, Large Language Models, Prompt Engineering, Apache Spark, Build Management, Scikit Learn, Debezium, Kubernetes, HuggingFace, Xgboost, Apache Kafka, Search Engines, Machine Learning Operations, Feature Extraction, Data Pipelines - **Published:** September 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a562a6784ed8a2d7 ## About the Role * 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). ## Description What You'll Do Tekmetric sits on a decade of granular operational data from thousands of automotive repair shops - millions of operational records spanning customer history, service patterns, and technician activity. We are building the ML layer on top of it: retrieval systems, recommendation models, embedding infrastructure, and the LLM integration layer that makes AI features feel native to the product workflow. This is a systems role for someone who builds production AI seriously. * 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., * 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. ## Related Videos - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Shoot for the moon - machine learning for automated online ad detection](https://www.wearedevelopers.com/videos/502-shoot-for-the-moon-machine-learning-for-automated-online-ad-detection) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)