Senior AI/ML Engineer

Key Responsibilities:

• Design and implement foundational GenAI services: vector search, prompt tuning, agent orchestration.
• Build the underlying infrastructure for autonomous and semi-autonomous AI agents including support for agent collaboration, reasoning, and memory persistence.
• Build standardized APIs/SDKs that make it easy for product teams to compose, deploy, and monitor Generative AI workloads.

Generative AI Enablement:

• Stand up LLM runtimes with token/rate governance, caching, and safe tool-use.
• Implement RAG at scale: ingestion pipelines, chunking/embedding policies, hybrid search.
• Build agent orchestration (single & multi-agent) with planning, tool routing, shared/persistent memory.

Infrastructure & Automation:

• Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services.
• Automate data and model pipelines for RAG, LLM fine-tuning, and agent orchestration.
• Integrate observability tools (Datadog or equivalent) for real-time performance, drift detection and safety monitoring of AI outputs.

Safety, Security & Compliance Integration:

• Embed compliance-by-design (HIPAA/CLIA/CAP/FDA/GDPR): PHI/PII handling, encryption, access controls, audit trails
• Implement guardrails: input/output filters, prompt hardening, allow/deny policies for tool execution, policy-as-code in CI/CD
• Bias/explainability hooks and automated evaluations for RAG/LLM/agents; drift and regression detection