Raynmaker Inc
About Raynmaker
Raynmaker.ai is the AI-native sales engine purpose-built for small and mid-sized businesses. We empower local and franchise businesses to compete with enterprise-level capabilities—through AI-driven lead targeting, next-best-action automation, and intuitive workflows that help them close more deals, faster. We're a venture-backed, fast-growing team committed to helping SMBs grow with confidence.
Role Overview We’re seeking a
Senior Data / ML / AI Engineer
to architect and build the intelligence layer of our autonomous sales platform. This role is responsible for designing, implementing, and optimizing the ML, LLM, scoring, retrieval, and agent-based systems that power live customer interactions and real business outcomes.
You will work closely with technology leadership to convert AI concepts into scalable, production-grade systems — including RAG pipelines, reinforcement-learning-based decision systems, vectorized knowledge bases, custom LLM deployments, real-time streaming inference, and multi-tenant data pipelines.
If you are a senior engineer who can bridge ML science, distributed systems, and pragmatic productionization, this role will put you at the core of a first-of-its-kind AI-native platform.
Key Responsibilities LLM, RAG & Agent Systems
Design, develop, and optimize
RAG pipelines
with high-performance vector databases (Milvus, Zilliz, Pinecone, Weaviate).
Build
scoring, ranking, and predictive models
that drive real-time decision-making for sales and customer interactions.
Develop and refine
agent-driven architectures , including tool calling, memory management, and multi-step reasoning flows.
Deploy, fine‑tune, and optimize
custom LLMs , ensuring cost efficiency and performance at scale.
Enrich internal knowledge bases and embeddings using advanced ML techniques.
Machine Learning Engineering & Data Infrastructure
Build large-scale
data ingestion, transformation, and real-time streaming
pipelines for model training and inference.
Implement
reinforcement learning systems
that 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.
Production Systems & Platform Integration
Architect and maintain
microservices
exposing ML/LLM capabilities through secure APIs.
Work with real-time systems:
voice, streaming, WebSockets , and other live interaction pipelines.
Ensure multi-tenant data isolation, configuration management, and performance scaling.
Collaborate cross‑functionally to define data contracts, agent flows, and platform intelligence requirements.
Required Skills
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.
Nice to Have
Experience with
streaming data systems
(Kafka, Kinesis, Pulsar).
Experience with
model monitoring, drift detection, and automated evaluation
Background with
AWS ML stack
(SageMaker, Bedrock, EKS, Lambda).
Experience with model compression, quantization, or accelerated inference.
Familiarity with CRM data patterns or real-time ingestion (Salesforce, HubSpot, Zoho).
We are committed to fostering a diverse, inclusive, and equitable workplace where all individuals are valued, respected, and empowered, regardless of their background, identity, or beliefs. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
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Role Overview We’re seeking a
Senior Data / ML / AI Engineer
to architect and build the intelligence layer of our autonomous sales platform. This role is responsible for designing, implementing, and optimizing the ML, LLM, scoring, retrieval, and agent-based systems that power live customer interactions and real business outcomes.
You will work closely with technology leadership to convert AI concepts into scalable, production-grade systems — including RAG pipelines, reinforcement-learning-based decision systems, vectorized knowledge bases, custom LLM deployments, real-time streaming inference, and multi-tenant data pipelines.
If you are a senior engineer who can bridge ML science, distributed systems, and pragmatic productionization, this role will put you at the core of a first-of-its-kind AI-native platform.
Key Responsibilities LLM, RAG & Agent Systems
Design, develop, and optimize
RAG pipelines
with high-performance vector databases (Milvus, Zilliz, Pinecone, Weaviate).
Build
scoring, ranking, and predictive models
that drive real-time decision-making for sales and customer interactions.
Develop and refine
agent-driven architectures , including tool calling, memory management, and multi-step reasoning flows.
Deploy, fine‑tune, and optimize
custom LLMs , ensuring cost efficiency and performance at scale.
Enrich internal knowledge bases and embeddings using advanced ML techniques.
Machine Learning Engineering & Data Infrastructure
Build large-scale
data ingestion, transformation, and real-time streaming
pipelines for model training and inference.
Implement
reinforcement learning systems
that 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.
Production Systems & Platform Integration
Architect and maintain
microservices
exposing ML/LLM capabilities through secure APIs.
Work with real-time systems:
voice, streaming, WebSockets , and other live interaction pipelines.
Ensure multi-tenant data isolation, configuration management, and performance scaling.
Collaborate cross‑functionally to define data contracts, agent flows, and platform intelligence requirements.
Required Skills
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.
Nice to Have
Experience with
streaming data systems
(Kafka, Kinesis, Pulsar).
Experience with
model monitoring, drift detection, and automated evaluation
Background with
AWS ML stack
(SageMaker, Bedrock, EKS, Lambda).
Experience with model compression, quantization, or accelerated inference.
Familiarity with CRM data patterns or real-time ingestion (Salesforce, HubSpot, Zoho).
We are committed to fostering a diverse, inclusive, and equitable workplace where all individuals are valued, respected, and empowered, regardless of their background, identity, or beliefs. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
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