Note: The job is a remote job and is open to candidates in USA. Tekmetric provides cloud-based software that helps automotive repair shops manage operations, payments, customer engagement, and growth. The Senior Machine Learning Engineer will build production AI systems, including retrieval-augmented generation pipelines, semantic search, recommendation models, embedding infrastructure, evaluation frameworks, and AWS-based ML integrations.
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
Skills
- 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
Benefits
- Hybrid and remote work models based on proximity to office hubs
- Travel to team and company-wide offsites several times a year is fully supported
- Generous Paid Time Off
- Paid maternity, parental bonding, and medical leave for employees or their loved ones
- Comprehensive health benefits, including Medical, Dental, Vision, and Prescription coverage
- Employee-only health plans covering 100% of premiums
- Family health plans covering 50% of costs
- Free, confidential counseling through BetterHelp
- 401(k) Retirement Savings Plan with 100% employer match on contributions up to 6%
- Flexible Spending Accounts (FSA)
- Health Savings Accounts (HSA)
- Life and Accidental Death & Dismemberment (AD&D) Insurance
- Up to $60/month toward fitness, mental health, or almost anything that helps you feel your best
- After one year of employment, a $300 home office setup bonus
- Support for continuing education
- Fun activities
Company Overview
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