The Role
Design, build, and productionize internal enterprise AI solutions (LLMs, agentic workflows, RAG, MLOps). Develop Python-based AI systems, integrate with enterprise platforms, ensure governance, observability, and scalability, support adoption and cross-functional partnerships, and prototype emerging AI technologies.
Summary Generated by Built In
Data Scientist
Primary Skills
- Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Specialization
- Data Science Advanced: Data Specialist
Job requirements
- AI Developer Anaplan Key Responsibilities AI Engineering & Development • Build and deliver internal enterprise AI solutions — including LLM integrations, agentic AI workflows, Gemini Enterprise features, and supporting data pipelines — designed for the scale, security, and reliability requirements of a global workforce. • Develop code in Python following the AI engineering standards, coding practices, and technical guardrails. • Participate in technical design reviews and contribute implementation input during intake, design, and production deployment phases. • Evaluate and prototype new AI tools, frameworks, and platforms, providing input on findings to support technical recommendations. • Contribute to the development and maintenance of the LLM abstraction layer and multi-model integrations. • Support technical debt remediation, system observability, and scalability improvements for Anaplan’s internal AI systems. AI Platform Governance & Enablement • Support the AI intake process by contributing technical assessments of feasibility, build complexity, and integration requirements for employee-facing tools. • Ensure all assigned internal AI initiatives meet engineering, security, compliance, and responsible AI standards before and during development. • Develop AI solutions against clear technical specifications • Build and maintain integrations across core enterprise systems (Salesforce, ServiceNow, Gainsight, Workday) as directed. • Implement human-in-the-loop, explainability, and auditability features as required for decision-impacting AI systems. • Support AI agent lifecycle management including monitoring, feedback loops, and continuous improvement. AI Operations & Workforce Adoption • Contribute to the technical deployment and operationalization of AI agents and solutions, supporting production readiness and stability for Anaplan employees. • Support Gemini Enterprise adoption and participate in AI Friday sessions with technical demonstrations and knowledge sharing. • Help build and maintain engineering dashboards tracking internal AI system performance, adoption rates, and ROI metrics. • Communicate engineering decisions and solution outcomes clearly to technical peers and team leads. Innovation, R&D & Emerging Technology • Research and prototype emerging AI/ML techniques, large language models, agent frameworks, and enterprise tooling to identify internal productivity and cost-saving opportunities. • Contribute to proof-of-concept pilots that validate novel internal use cases before committing to full engineering investment. • Maintain and contribute to the AI innovation pipeline — a living backlog of high-potential experiments. Cross-Functional Technical Partnership • Work alongside the Transformation & Financial Flexibility team to support identification and technical validation of AI-driven cost-saving opportunities in Anaplan’s internal operations. • Act as a technical contributor and AI subject matter resource to business units identifying internal AI use cases. • Build collaborative working relationships across Engineering, Security, Compliance, Legal, and HR. Required Qualifications • 5+ years of software or AI/ML engineering experience, with a focus on building and shipping production-grade AI or data-driven applications. • Hands-on experience with the AI/ML stack: LLMs, retrieval-augmented generation (RAG), agent frameworks, or MLOps pipelines — with exposure to enterprise or workforce deployment contexts. • Experience developing and deploying AI applications in cloud environments, including reliability, observability, and performance considerations. • Proficiency in Python and familiarity with enterprise cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker) and AI orchestration tooling. • Exposure to Google Workspace AI and Gemini Enterprise, or equivalent enterprise AI platforms (e.g., Microsoft Copilot, OpenAI Enterprise). • Ability to work effectively within defined architectural standards and del
Skills Required
- 5+ years of software or AI/ML engineering experience building production-grade AI or data-driven applications
- Hands-on experience with LLMs, retrieval-augmented generation (RAG), and agent frameworks
- Experience with MLOps pipelines and AI engineering for enterprise deployment
- Proficiency in Python and Python engineering best practices
- Experience with cloud AI platforms (GCP Vertex AI, Azure AI, or AWS SageMaker)
- Exposure to Google Workspace AI and Gemini Enterprise or equivalent enterprise AI platforms (Microsoft Copilot, OpenAI Enterprise)
- Experience developing and deploying AI applications with reliability, observability, and performance considerations
- Familiarity with ML frameworks: TensorFlow, PyTorch, scikit-learn, Keras, MXNet, CNTK
- Experience integrating AI solutions with enterprise systems (Salesforce, ServiceNow, Gainsight, Workday)
- Familiarity with Kubeflow and BentoML for model orchestration and serving
- Experience with data validation and model monitoring tools (Great Expectations, Evidently AI)
- Statistical and forecasting methods (Hypothesis testing, regression, ARIMA/ARIMAX, exponential smoothing) and classification techniques (Decision Trees, SVM)
- Experience with big-data tooling such as PySpark
- Experience with enterprise AI governance, security, compliance, and responsible AI practices
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
