Senior Data Engineer, ML, AI
RaYnmaker
full-time
Posted on:
Location Type: Office
Location: Austin • Texas • 🇺🇸 United States
Visit company websiteSalary
💰 $180,000 - $200,000 per year
Job Level
Senior
Tech Stack
Distributed SystemsDockerKubernetesMicroservicesNoSQLPythonSQL
About the role
- Architect and build the intelligence layer of our autonomous sales platform.
- Design, implement, and optimize the ML, LLM, scoring, retrieval, and agent-based systems.
- Work closely with technology leadership to convert AI concepts into scalable systems.
- Design, develop, and optimize RAG pipelines with high-performance vector databases.
- Build scoring, ranking, and predictive models for real-time decision-making.
- Develop and refine agent-driven architectures, including tool calling and multi-step reasoning.
- Deploy, fine-tune, and optimize custom LLMs for cost efficiency and performance.
- Enrich internal knowledge bases and embeddings using advanced ML techniques.
- Build large-scale data ingestion, transformation, and real-time streaming pipelines.
- Implement reinforcement learning systems to improve agent behaviors over time.
- Own ML model lifecycle: development, evaluation, deployment, optimization, and monitoring.
- Drive LLM cost optimization, including token efficiency, caching, and inference routing.
- Architect and maintain microservices exposing ML/LLM capabilities through secure APIs.
- Collaborate cross-functionally to define data contracts, agent flows, and platform intelligence requirements.
Requirements
- 7+ years of ML Engineering experience in production environments.
- Expert-level Python for ML workflows, backend services, and data pipelines.
- Strong experience with vector databases (Milvus, Zilliz, Pinecone, Weaviate).
- Experience building and deploying reinforcement learning systems.
- Deep hands-on experience with LLMs, RAG, prompting, scoring models, and tool calling.
- Experience with LangChain / LangGraph and modern LLM orchestration frameworks.
- Proven ability to design and optimize large-scale ML data pipelines.
- Production experience with real-time systems (voice, streaming, WebSockets).
- Proficiency with SQL and NoSQL databases.
- Strong understanding of microservices architecture, distributed systems, and event-driven workflows.
- Proficiency with Docker & Kubernetes for deployment and orchestration.
- Experience delivering custom LLM deployments in production.
- Ability to collaborate with engineering leadership and turn concepts into shipped capabilities.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learninglarge language modelsreinforcement learningdata pipelinesvector databasesPythonSQLNoSQLmicroservices architectureevent-driven workflows
Soft skills
collaborationcommunicationproblem-solvingleadershipoptimization