Senior AI Engineer
Team: Data Science & AI
Reports to: Head of AI
Location: US - Remote
Type: Full-time or Contract-to-Hire, Individual Contributor
Role Summary
The Senior AI Engineer is a core technical contributor within the Data Science organization, responsible for developing and maintaining the data, experimentation, and orchestration infrastructure that underpins our advanced AI research and prototype applications. This role bridges the gap between research and product by transforming bench-level experimental workflows into robust, testable, and scalable software systems.
The engineer will collaborate closely with Principal Data Scientists to design, implement, and iterate on systems supporting retrieval-augmented generation (RAG), knowledge graph–based reasoning, and other structured retrieval methods aimed at improving the contextual fidelity and performance of our AI Expert products. They will also coordinate with Platform Engineering and other core neuRealities product teams to ensure smooth handoff and integration of prototype systems into the broader neuRealities platform. Additionally, this role is a core contributor to the design and execution of our research strategy for other future products.
Core Responsibilities
1. AI Infrastructure and Data Systems
Design and implement scalable data pipelines and orchestration frameworks supporting AI research and prototype development and deployment.
Manage data ingestion, transformation, and enrichment workflows, including knowledge graph construction, embedding generation, and vector database integration.
Build and maintain tools for data provenance, lineage tracking, and versioned datasets used in AI experimentation.
Develop APIs and middleware for dynamic retrieval from structured (e.g., graph) and unstructured (e.g., text embeddings) data sources.
2. Experimentation & Prototyping
Build and operate experimental environments for rapid evaluation of models, retrieval strategies, generative techniques, and reasoning pipelines.
Partner with data scientists to translate research hypotheses into executable, instrumented software experiments.
Work with data scientists to develop metrics, evaluations and guardrails for RAG and hybrid retrieval models (latency, recall, faithfulness, grounding).
Support the creation of proof-of-concept (PoC) and minimum viable product (MVP) systems demonstrating novel functionality to internal and external customer stakeholders.
3. Software Engineering & Integration
Write and maintain production-grade, testable Python codebases, emphasizing modularity, composability, and reproducibility.
Develop and evaluate integrations with third party products such as voice cloning and digital human / avatar products.
Implement CI/CD workflows and deployment automation for AI components in collaboration with Platform Engineering.
Profile and optimize data flow, retrieval latency, and pipeline efficiency across distributed systems.
Contribute to and help maintain shared libraries and internal SDKs for use by other AI engineers and data scientists, as well as by platform engineers.
4. Cross-Team Collaboration
Serve as the technical liaison between Data Science and Platform Engineering, ensuring prototype architectures can be industrialized, maintained, and supported.
Provide documentation, interface contracts, and observability standards for experimental systems moving toward production.
Participate in technical design reviews and architecture discussions related to AI infrastructure and orchestration.
Mentor junior engineers and research associates on engineering best practices and reproducible experimentation.
Collaborate with Go-to-Market teams in providing technical information to customers and prospects and responding to technical questions as they arise.
Technical Stack Expectations
Languages:
Python (primary), including familiarity with common ML/AI libraries such as HF Transformers, TensorFlow, PyTorch, Scikit-learn and associated data processing libraries such as Pandas and NumPy.
Javascript + Typescript for API integrations and occasional front-end / UI development. Familiarity with modern UI / web app frameworks such as React and Next.js (or other similar modern frameworks) preferred.
Familiarity with other languages commonly used in AI work such as Rust and Go is a plus but not required.
Proficiency in other languages such as C/C++, Java, C# is a plus but not required.
Data & Orchestration: Familiarity with frameworks such as Airflow, Tika, Prefect, Ray, Spark a plus but not required
Storage & Retrieval: Strong database skills and familiarity with modern vector / graph / semantic data platforms such as Neo4j, Weaviate, Pinecone, ElasticSearch, or equivalent.
LLM & RAG Frameworks: LangChain, LlamaIndex, custom orchestration layers.
Infrastructure: Docker, Kubernetes, cloud-native environments (Azure / AWS / GCP).
Experimentation:
Familiarity with commercial and open source large language models (e.g OpenAI gpt models, Anthropic Claude models, Meta LLaMa models), embeddings models (e.g. OpenAI, Nvidia models), computer vision / object detection / VLM models (e.g. GPT-4V, CLIP, YOLO, SAM) is preferred.
Familiarity with common AI APIs such as OpenAI’s Chat Completions or Responses APIs is preferred.
Qualifications
Experience: 8+ years in software or ML engineering, with at least 2 years building production-grade or research-oriented AI systems.
Education: Degree in Computer Science, Applied Mathematics, or related technical discipline (or equivalent practical experience).
Proven experience in:
Building and scaling data pipelines and orchestration systems.
Integrating and optimizing graph and vector data stores.
Collaborating in cross-functional research–engineering environments.
Strong understanding of retrieval-augmented architectures, prompt engineering pipelines, and semantic data modelling.
Demonstrated ability to translate research concepts into maintainable, testable engineering solutions.