Principal AI Solution Engineer / AI Architect

Syneos Health is seeking a Principal AI Solution Engineer / AI Architect with a minimum of 8 years of overall technology experience, a strong foundation in data science and machine learning, and a record of designing and delivering multiple production-grade AI solutions.

The ideal candidate combines solid grounding in statistics, machine learning, and model evaluation with deep expertise in Azure AI Foundry, Azure OpenAI, Azure AI Search, retrieval-augmented generation (RAG), and agent-based systems, along with strong enterprise integration skills across REST APIs, Oracle Database, Oracle APEX, and the Microsoft ecosystem.

Key Responsibilities

• Own the end-to-end architecture and technical delivery of enterprise AI solutions, from requirement discovery and proof of concept through verification, validation, production rollout, and continuous improvement.
• Lead requirement analysis with business and domain stakeholders to understand the problem, users, data, constraints, risks, and measurable outcomes, and translate them into clear functional, non-functional, and acceptance criteria.
• Assess each use case and determine whether it is best served by traditional machine learning, generative AI, agentic AI, deterministic rules, or a hybrid approach, documenting the rationale, trade-offs, and risks behind each architecture decision.
• Partner with business, product, data, security, infrastructure, and engineering stakeholders to identify and prioritize high-value use cases and align solutions with enterprise architecture, data, and security standards.
• Architect machine learning solutions with data science teams, covering problem formulation, data and feature pipeline design, model selection, training and validation strategy, deployment, monitoring, and retraining.
• Design secure, scalable, reliable, and cost-conscious solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, and appropriate agent frameworks.
• Architect RAG solutions, including document ingestion, chunking, embeddings, vector indexing, hybrid retrieval, reranking, grounding, citation, and response-generation strategies.
• Design AI agents and agentic workflows using tool calls, orchestration, state and memory patterns, guardrails, exception handling, and human-in-the-loop approvals.
• Integrate AI capabilities with enterprise applications and data sources through REST APIs, Oracle Database, Oracle APEX, and Microsoft ecosystem services.
• Establish reference architectures, reusable components, engineering standards, prompt patterns, evaluation methods, and deployment practices for AI solutions.
• Define and implement controls for responsible AI, data privacy, security, access management, content safety, auditability, and regulatory or organizational compliance.
• Own verification and validation of implemented solutions, confirming through test plans, requirement traceability, benchmark datasets, statistical evaluation, and user acceptance testing that each solution is built correctly and meets the business need before release.
• Create evaluation frameworks covering model performance, answer quality, retrieval relevance, groundedness, safety, latency, reliability, user adoption, and cost, and use post-deployment monitoring to revalidate and improve solutions iteratively.
• Provide hands-on technical guidance, conduct architecture and code reviews, troubleshoot complex issues, and help teams make pragmatic build-versus-buy decisions.
• Mentor senior engineers, data scientists, and AI champions, accelerate capability development across application teams, and promote disciplined experimentation and knowledge sharing.
• Communicate architecture decisions, trade-offs, delivery risks, and business value clearly to both technical teams and senior leadership.
• Contribute to Agile planning, estimation, dependency management, release readiness, production support, and post-implementation reviews.

Required Skills

• AI Platforms and Solution Architecture:
• Proven ability to translate business requirements into solution architectures and to select between machine learning, generative AI, agentic, rule-based, and hybrid patterns based on the problem, available data, risk, explainability, and cost.
• Strong hands-on experience with Azure AI Foundry and Azure OpenAI for building, evaluating, deploying, and operating generative AI solutions.
• Experience with Azure AI Search, including vector search, hybrid search, semantic ranking, indexing strategies, filters, and security-aware retrieval.
• Practical experience with agent frameworks and orchestration patterns for enterprise use cases.
• Ability to design cloud-based AI architectures that balance scalability, availability, security, performance, maintainability, and cost.
• Data Science and Machine Learning:
• Strong foundation in statistics, probability, experimental design, and hypothesis testing, applied to model evaluation and decision-making.
• Hands-on experience with supervised and unsupervised learning, including classification, regression, clustering, time-series forecasting, and anomaly detection, using libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Solid understanding of data quality, feature engineering, training and validation strategies, overfitting, data leakage, model calibration, and performance measures suited to the business problem.
• Ability to interpret model outputs, explain model behavior using explainability techniques, and assess bias and fairness.
• Retrieval-Augmented Generation (RAG):
• Deep understanding of embeddings, vector databases, retrieval architectures, and grounding strategies.
• Experience designing ingestion and retrieval pipelines for structured and unstructured enterprise content.
• Knowledge of chunking, metadata design, query transformation, hybrid retrieval, reranking, citation, access controls, and freshness strategies.
• Ability to evaluate and improve retrieval relevance, answer groundedness, completeness, and hallucination risk.
• Agent Development:
• Understanding of single-agent and multi-agent workflows, agent orchestration, planning, routing, state, memory, and task decomposition.
• Experience with function or tool calls, structured outputs, API-based actions, error recovery, and observability.
• Ability to design human-in-the-loop approval and escalation controls for sensitive or high-impact actions.
• Knowledge of guardrails that prevent unauthorized, unsafe, or unintended agent behavior.
• Prompt Engineering, Evaluation, and Validation:
• Advanced prompt engineering skills, including system instructions, few-shot prompting, decomposition, structured output, grounding, and reusable prompt templates.
• Experience designing automated and human evaluation approaches, test datasets, quality rubrics, regression tests, and production feedback loops.
• Experience defining verification and validation plans for machine learning, generative AI, and agentic solutions, including requirement traceability, acceptance criteria, test coverage, benchmark datasets, and sign-off evidence.
• Ability to apply statistical rigor to evaluation, including sampling strategies, confidence intervals, significance testing, and inter-rater agreement for human review.
• Enterprise Integration:
• Strong experience designing and integrating REST APIs and JSON-based services, including authentication, authorization, throttling, resilience, and error handling.
• Working knowledge of Oracle Database, SQL, data access patterns, and secure integration with enterprise data.
• Experience integrating AI capabilities into Oracle APEX or comparable enterprise and low-code application platforms.
• Good understanding of the Microsoft ecosystem and how AI solutions integrate with Azure services, identity, security, monitoring, and collaboration platforms.
• Engineering, DevOps, and Operations:
• Strong software engineering fundamentals and proficiency in at least one enterprise programming language, preferably Python, JavaScript or TypeScript.
• Experience with Git, automated testing, CI/CD, infrastructure and environment configuration, secrets management, and production release controls.
• Experience implementing logging, tracing, monitoring, usage analytics, quality telemetry, and cost monitoring for AI workloads.
• Understanding of API security, identity and access management, encryption, privacy, and secure software development practices.