AI/ML Engineer

Safe-Guard Products International LLC•Atlanta, GA

2h•Hybrid

About The Position

We are building an enterprise-wide GenAI capability that combines top-down platform governance with bottom-up employee enablement to drive responsible, scalable AI adoption across the organization. As an AI/ML Engineer focused on GenAI platforms and employee-facing enablement, you will design reusable GenAI services, establish standards, and empower employees to safely use AI through tools such as Microsoft 365 Copilot and internal GenAI applications. This role is highly product-facing and cross-functional, working with IT, security, product, and business teams. You will operate across Azure OpenAI, AWS Bedrock, and GCP Vertex AI, and play a key role in expanding GenAI usage from centralized platforms down to everyday employee workflows.

Requirements

3–4 years of experience in AI/ML engineering, applied data science, or related roles.
Strong proficiency in Python and SQL.
Solid foundation in machine learning (classification, anomaly detection, model evaluation).
Hands-on experience with GenAI systems, including: Prompt design and testing Embeddings and vector search Retrieval-Augmented Generation (RAG) Structured outputs and validation
Experience building and deploying APIs/services (FastAPI or similar).
Familiarity with containerization (Docker) and CI/CD.
Experience working in cloud environments (Azure, AWS, and/or GCP).
Strong communication skills and comfort working in product-facing and enablement roles.
Experience building internal platforms, shared services, or enablement frameworks.
Exposure to M365 Copilot extensibility or enterprise productivity tooling.
Familiarity with ML/LLMOps tools (model registries, experiment tracking, prompt/version management).
Knowledge of Responsible AI, governance, and enterprise data privacy practices.
Experience in regulated industries (insurance, auto warranty, financial services).

Responsibilities

Design and implement enterprise GenAI platform components, including: LLM orchestration (LangChain, LangGraph) and provider abstraction (Azure OpenAI, AWS Bedrock, GCP Vertex) Prompt and workflow templates Evaluation frameworks and telemetry Guardrails for safety, compliance, and data protection
Define standardized GenAI patterns that product and engineering teams can reuse across the organization.
Partner with architecture, security, and compliance teams to ensure GenAI solutions align with enterprise policies and Responsible AI principles.
Establish technical standards and best practices for GenAI development across onshore and offshore teams.
Support experimentation by business users while ensuring guardrails, monitoring, and auditability.
Work with business stakeholders to identify high-impact employee workflows where GenAI can improve productivity, decision-making, and quality.
Partner with IT and business teams to expand and optimize the use of M365 Copilot across employee roles (operations, customer service, leadership, support teams).
Design and implement integrations that connect enterprise data, knowledge bases, and workflows to M365 Copilot using approved patterns.
Build ML and GenAI solutions that support multiple business domains, including: Claims loss mitigation and fraud detection Customer and agent experience Operational efficiency and internal productivity
Combine classical ML, rules engines, and LLMs into hybrid systems where appropriate.
Ensure solutions provide explainability, traceability, and measurable business value.
Build enterprise-grade RAG pipelines over structured and unstructured data (policies, procedures, internal documentation, historical records).
Implement document ingestion, chunking, embeddings, re-ranking, and citation-based outputs.
Ensure GenAI responses are grounded, accurate, and auditable.
Define and implement evaluation strategies for ML and GenAI solutions, including: Accuracy, precision/recall, hallucination rates Latency and cost metrics User adoption and satisfaction
Build monitoring and feedback loops to continuously improve AI systems.
Apply Responsible AI practices: PII protection, access controls, logging, and audit trails.