Build the AI enterprises can truly own.

The role

Translate business workflows, data sensitivity, governance expectations, and infrastructure constraints into secure, scalable architectures spanning data, models, orchestration, integrations, deployment, observability, and human oversight.

What you will do

• Lead technical discovery with enterprise executives, architects, engineering leaders, and business stakeholders.
• Define end-to-end private AI architectures across identity, policy, data, tools, models, orchestration, validation, workflow, and learning.
• Shape provider-neutral model strategies spanning deterministic rules, SLMs, private RAG, specialist models, and larger LLMs.
• Design secure enterprise integrations, governance controls, model routing, and on-premises, private-cloud, hybrid, or disconnected deployments.
• Guide AI and Data Engineers through design, build, testing, production readiness, and technical risk management.
• Create reusable reference architectures, accelerators, templates, and technical playbooks.

What you bring

• 10–15 years in technology, including 5+ years in AI, ML, data, cloud, platform, or enterprise architecture.
• 2–3+ years designing production GenAI, LLM, SLM, RAG, or AI orchestration systems.
• Strength in distributed systems, model routing, orchestration, secure RAG, evaluation, and human-in-the-loop patterns.
• Practical knowledge of private deployment, GPU infrastructure, Kubernetes, model serving, IAM, encryption, isolation, and observability.
• Experience leading enterprise design reviews and customer implementations; regulated-industry experience is strongly preferred.

You will thrive here if: you combine deep technical judgment with commercial awareness and can move comfortably between executive conversations and hands-on engineering decisions.

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The role

Design and build the secure foundation that turns structured and unstructured enterprise information into trusted, permission-aware, traceable context for private AI orchestration and SLMs.

What you will do

• Build secure ingestion from ERP, CRM, DMS, databases, APIs, spreadsheets, repositories, and legacy applications.
• Create canonical models and pipelines for private RAG, enterprise search, AI workflows, evaluation, analytics, and fine-tuning.
• Develop document processing for contracts, claims, policies, manuals, emails, PDFs, and other unstructured sources.
• Implement metadata, lineage, versioning, source traceability, vector and keyword indexes, and permission-aware retrieval.
• Establish automated data quality, reconciliation, schema validation, anomaly monitoring, isolation, encryption, and masking.
• Document source systems, transformations, data contracts, dependencies, runbooks, and production operations.

What you bring

• 5–8 years of hands-on data engineering, with 2+ years supporting AI, ML, search, analytics, or RAG.
• Strong Python and advanced SQL plus production ETL/ELT and orchestration experience.
• Hands-on experience with tools such as Airflow, Dagster, dbt, Spark, Kafka, PostgreSQL, Snowflake, or Databricks.
• Knowledge of vector databases, embeddings, search indexes, data modelling, lineage, observability, and secure integration patterns.
• Production experience across cloud, private cloud, hybrid, or on-premises environments; regulated-data experience is preferred.

You will thrive here if: you know enterprise AI quality depends as much on data quality, permissions, metadata, lineage, and reliability as it does on model capability.

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The role

Work between applied AI research and production software engineering, moving use cases from rapid prototype to reliable, secure enterprise deployment.

What you will do

• Build private AI workflows for retrieval, document processing, extraction, classification, summarization, recommendations, and workflow assistance.
• Implement secure RAG and task-specific SLM solutions over enterprise documents, databases, policies, contracts, tickets, and knowledge stores.
• Develop model routing across rules, specialist SLMs, retrieval models, and larger LLMs.
• Fine-tune, adapt, quantize, and optimize models for customer terminology, tasks, and infrastructure.
• Build validation, fallback, retries, timeouts, human escalation, evaluation, citations, version controls, and observability.
• Contribute reusable orchestration components, connectors, evaluators, guardrails, and deployment components.

What you bring

• 4–7 years in software engineering, ML, NLP, or applied AI, including 2+ years with LLMs, SLMs, RAG, or orchestration.
• Strong Python, software engineering, PyTorch, Hugging Face, structured outputs, function calling, and tool use.
• Experience with retrieval, orchestration frameworks, model serving, fine-tuning, quantization, and inference optimization.
• Production skills across FastAPI, APIs, event-driven services, Docker, Kubernetes, CI/CD, MLOps or LLMOps, tracing, and monitoring.
• Demonstrated production deployment experience; work with enterprise APIs or customer-facing AI products is preferred.

You will thrive here if: you experiment quickly while maintaining the discipline needed for secure, observable, testable, and maintainable enterprise systems.

Apply for this role