Principal Software Engineer

Senior Software Engineer

Full-Time · Remote (U.S.)

Scroll down to find the complete details of the job offer, including experience required and associated duties and tasks.

About Velastegui Ventures

We build enterprise AI infrastructure that lets regulated organizations query their unstructured and structured data in natural language. Our platform combines retrieval-augmented generation, federated search, and self-hosted model inference to deliver citation-backed, auditable answers — deployed inside the customer's own cloud boundary. We're growing the engineering team to scale this platform through its first enterprise deployments and into a multi-tenant product.

About the Role

You'll design, build, and operate the backend of our Enterprise Knowledge and Natural Language Query Platform: ingestion, retrieval, inference, and the governance layer that makes AI defensible for regulated customers. This is a hands-on senior role with real architectural ownership — you'll ship on the platform as it stands today while helping design the next generation of it. We're looking for someone comfortable operating anywhere from 5 to 15 years into their career, provided the depth is there.

Where the platform is today, and where it's going

Today, ingestion runs on SQS with Ray-Serve workers, retrieval uses Amazon Bedrock and Bedrock Knowledge Bases inside the customer's AWS boundary, and the platform is deployed via a GitOps supply chain (ArgoCD, Cosign, Kyverno) into customer-owned EKS.

Over the next 12–18 months we're evolving toward a Kafka-based streaming backbone with CDC, self-hosted LLM inference (vLLM / SGLang), self-hosted vector and graph retrieval, and federated search across semantic, lexical, and knowledge-graph modalities. You'll work on both sides of that transition.

The first 6 months

You'll help stand up our first BYOC deployment into an enterprise client on the current SQS + Bedrock stack, own one or two of the six data-source connectors end-to-end, and drive the ingestion pipeline's incremental-sync design.

Expect direct contact with the customer's platform and compliance teams. In parallel, you'll help shape the Kafka-based connector framework and self-hosted inference path that lands on the platform later in the year.

What You'll Do

• Design and operate backend services across an event-driven microservices architecture — ingestion, retrieval, inference, and the connector framework that onboards new data sources.

• Ship on the current SQS + Ray-Serve + Bedrock stack while helping design and land the Kafka + CDC + self-hosted-inference generation of it.

• Extend retrieval quality across semantic, lexical, and (eventually) graph modalities, including fusion and re-ranking.

• Grow the AI serving path: embedding, generation, re-ranking, model routing, and the registries and evals that let us promote models safely.

• Build observability and evaluation into every service you own — tracing, metrics, and quality/drift signals, not just system health.

• Partner with security and compliance on the controls that make SOC 2 and (where applicable) HIPAA and GDPR defensible: encryption, access control, audit lineage, data residency.

• Support enterprise deployments alongside customer platform teams — IaC rollout, cutover, rollback, and the day-two handoff.

Required

• 5+ years building and operating production backend systems, with meaningful ownership of distributed architecture.

• Strong proficiency in at least one of Go, Rust, or Python, and comfort working across a polyglot codebase.

• Solid grounding in distributed systems: asynchronous messaging, event-driven design, and the failure modes that come with them. Direct experience with a streaming or queue technology (Kafka, SQS, RabbitMQ, or similar) and the concepts

behind Change Data Capture.

• Working knowledge of PostgreSQL and at least one non-relational store (vector, document, or graph).

• Fluency with AWS (or equivalent), containers (Docker/Kubernetes), and infrastructure-as-code (Terraform or similar).

• Real interest in — ideally hands-on experience with — applied AI/ML systems: embeddings, vector search, RAG, or LLM serving. This is an AI infrastructure role; curiosity here isn't optional.

• Communication skills to work directly with customers, compliance stakeholders, and product.

Nice to Have (any of these move you up the stack)

• Managed AI services and hosted retrieval (Amazon Bedrock, Bedrock Knowledge Bases, OpenAI / Anthropic APIs) — relevant to the current stack.

• Self-hosted LLM serving (vLLM, SGLang, llama.cpp) or OpenAI-compatible gateways — relevant to where we're going.

• Vector databases and hybrid semantic/lexical retrieval (Qdrant, Weaviate, Pinecone, OpenSearch hybrid).

• Graph or knowledge-graph modeling (Neo4j or similar).

• Large-scale data processing (Ray, Spark, Databricks) and lakehouse patterns.

• OpenTelemetry-based observability and LLM tracing/eval tooling.

• Enterprise IAM (SSO/SAML/OIDC/SCIM) and multi-tenant SaaS.

• Shipping in SOC 2 / HIPAA / GDPR environments, or prior forward-deployed engineering work.

Compensation & Logistics

• Cash compensation: [$210,000 – $250,000] based on experience and location. No equity component.

• Remote within the U.S. Expect an on-call rotation as we support production customers.

• Background check required; the role handles data from customers in regulated industries.

Why Velastegui Ventures

• Ship into a real enterprise deployment in your first year, not a hypothetical roadmap.

• Own architecture on a platform built ground-up for regulated, enterprise-scale AI — not retrofitted from a chatbot demo. xhyhwjd

• Small team, direct customer contact, senior autonomy.