Senior AI Developer in Houston, Texas at myDNA, Inc.

OUR PURPOSE

Our mission is to build a healthier and more connected world with precision health and genealogy services.

We empower individuals with actionable insights into their genetic makeup, fostering a deeper understanding of their ancestry, health, and wellness. By integrating the experience of Gene by Gene Laboratory Services, FamilyTreeDNA genealogy, and myDNA reporting services, we strive to deliver cutting-edge genetic testing and personalized solutions that inspire informed decisions and enhance quality of life. Our team is dedicated to advancing the field of genomics through innovation, research, and a commitment to excellence.

OUR VALUES

All employees are expected to demonstrate our values of Innovate, One Team, and Integrity when carrying out the accountabilities and responsibilities of their role.

This how we show up every day for ourselves, our colleagues and our customers and strategic partners to deliver our vision and strategic goals.

POSITION OVERVIEW

We are seeking a Senior AI Developer to join our engineering team. In this senior role you will help shape and execute our AI/ML strategy, guiding our journey from early generative-AI capabilities into a mature, production-grade AI practice. You will integrate generative-AI features into our products and internal platforms, combine retrieval-augmented generation (RAG) with knowledge graphs to ground model outputs in our domain data, and own AI features end-to-end — model selection, prompt engineering, retrieval, fine-tuning where appropriate, deployment, observability, cost governance, and compliance posture. As a senior individual contributor, you will also provide architectural direction and code-level guidance to existing engineering teams who own day-to-day delivery of supporting backend and data-layer work.

COMPLIANCE & GOVERANCE

This role operates in a regulated environment. The Senior AI Developer is expected to understand how regulatory obligations apply to AI/ML systems specifically — training data, PII handling, model output controls, audit logging, evidence retention, and the limits regulation places on third-party model usage — and to produce the operational evidence that carries our AI capabilities through audits.

ACCOUNTABILITIES AND RESPONSIBILITIES

• Helps set technical direction for AI/ML — evaluates models, frameworks, vector stores, graph databases, evaluation tooling, and orchestration patterns; makes recommendations and leads adoption.
• Designs and implements production generative-AI features using managed foundation-model services, applying guardrails, contextual grounding, structured output, tool use, and agentic workflow patterns.
• Builds retrieval-augmented generation (RAG) pipelines — document ingestion, chunking, embeddings, vector search, hybrid retrieval, and reranking — selecting the storage approach that best fits each use case.
• Designs and operates knowledge graphs to model the domain — schema and ontology design, entity resolution, relationship extraction, and integration with LLM workflows (GraphRAG, hybrid graph + vector retrieval).
• Trains and fine-tunes models where it produces measurable lift, including dataset preparation, supervised and parameter-efficient fine-tuning, baseline evaluation, and deployment.
• Provides architectural direction and code-level guidance to existing .NET and SQL engineering teams responsible for backend services and data-layer integration with AI features.
• Defines and enforces LLMOps / MLOps practices: prompt and model versioning, evaluation harnesses, regression testing, latency and cost SLOs, and reproducible training pipelines.
• Implements observability for AI systems and makes the data actionable across token usage, latency, hallucination and refusal rates, contextual-grounding faithfulness, cost-per-request, and quality metrics.
• Builds and operates AI systems for audit-readiness — data lineage, prompt and model version traceability, decision logging, access controls, and evidence collection.
• Mentors fellow engineers, leads code review, contributes to architecture decision records, and helps shape the team's AI engineering standards.
• Partners with security and compliance to ensure AI systems meet data privacy, PII handling, prompt injection defense, and responsible-AI requirements throughout the model lifecycle.

POSITION REQUIREMENTS

• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
• 10+ years of professional software engineering experience.
• 2+ years building production AI/LLM features on a managed foundation-model platform.
• Demonstrable experience training and/or fine-tuning models — supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), or classical ML — including dataset preparation, evaluation, and deployment.
• Production experience with knowledge graphs — schema and ontology design, a graph database, and at least one graph query language (Cypher, SPARQL, or Gremlin).
• Demonstrable production experience in regulated environments. Compliance is a hard requirement for this role.
• 3+ years of production cloud experience including at least one managed AI service.
• Solid grounding in prompt engineering, RAG, embeddings, vector search, guardrails, contextual grounding, and LLM evaluation methodology.
• Ability to provide architectural direction and technical guidance to existing engineering teams; senior IC influence rather than line management.
• Strong testing discipline — unit, integration, and contract testing, plus AI-specific evaluation harnesses.
• Excellent written and verbal communication; ability to explain AI tradeoffs to non-technical, legal, and compliance stakeholders.

OTHER COMPENTENCIES AND TECHNOLOGIES

• Hands-on experience with managed AI services across major cloud providers — for example Amazon Bedrock, Amazon SageMaker, Google Vertex AI, or Azure AI Foundry — is a plus. Familiarity across more than one provider is preferred.
• Production C# / .NET experience with ASP.NET Core and Entity Framework Core.
• Production SQL experience on Microsoft SQL Server and PostgreSQL — schema design, query tuning, indexing, and performance troubleshooting.
• Comfort working with on-premises database infrastructure and hybrid (on-prem / cloud) data architectures.
• Python proficiency for ML workflows.
• Production Infrastructure-as-Code experience (Terraform, CDK, CloudFormation, or Pulumi).
• Experience designing and consuming REST APIs, including modern authentication patterns (OAuth 2.0, OIDC, JWT).
• Broader machine learning experience: classical/predictive ML, deep learning frameworks, or experience with managed training platforms.
• GraphRAG patterns, entity resolution, and automated knowledge-graph construction from unstructured sources.
• Responsible-AI practices — bias evaluation, red-teaming, OWASP Top 10 for LLMs, prompt injection defense, NIST AI RMF, ISO/IEC 42001.
• Container experience (Docker, Kubernetes) and event-driven architecture experience.
• Experience supporting third-party audits — evidence collection and auditor-facing documentation.
• Open-source contributions, technical writing, conference talks, or research publications in AI/ML are a strong plus.
• Advanced degree (MS or PhD) in CS, ML, Statistics, or a related quantitative field is preferred.

WHY JOIN US

At Gene by Gene, you’ll join a mission-driven team advancing the science of genetics and discovery. You’ll have the opportunity to shape meaningful campaigns, tell compelling brand stories, and collaborate with talented professionals who share your passion for creativity, curiosity, and impact.