• Design, build, and ship production-grade GenAI features that help teams explore data, ask better questions, and trust the answers they get. • Own GenAI features end-to-end — from system design and LLM integration through deployment, monitoring, and iteration. • Build and maintain reusable GenAI platform components that make it easier and safer for teams across Teamworks to ship AI-powered features. • Establish and document best practices for building, testing, and operating GenAI systems reliably in production. • Enable product engineering teams to ship AI-powered features by providing shared infrastructure, technical guidance, and hands-on integration support. • Make architectural decisions that balance speed, scalability, reliability, security, and cost in a multi-tenant data environment. • Partner with Product, Design, and domain subject-matter experts to deliver trustworthy, explainable AI experiences that align with real user workflows.

• Design multi-agent architectures with robust state management, memory, and routing. • Choose and implement leading frameworks such as LangGraph/LangChain Agents, Microsoft AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack Agents—and justify trade-offs. • Build modular components (planners, tool registries, policy guards, evaluators) that are reusable across clients and domains. • Integrate enterprise tools and data sources via function/tool calling, webhooks, and event-driven flows (Queues/Service Bus/Functions). • Implement retrieval-augmented generation (RAG) patterns with vector stores (Azure AI Search, pgvector, MongoDB Atlas, Pinecone, Weaviate, Milvus) and structured knowledge (SQL/Graph). • Add deterministic fallbacks, circuit breakers, and caching to keep latency and cost predictable. • Define SLIs/SLOs for agent runs; implement tracing, metrics, and logging (e.g., Langfuse + OpenTelemetry) and build dashboards for run-level analytics. • Create evaluation harnesses (automatic + human-in-the-loop) using tools such as Ragas, DeepEval, promptfoo to measure groundedness, task success, safety, and cost. • Productionize with CI/CD, environment promotion, feature flags, and canary strategies; optimize cost-per-task and time-to-success. • Enforce content and safety policies (redaction, classification, guardrails) with policy-as-code; implement role/tenant isolation and data minimization. • Collaborate with security teams to align to ISO 27001/SOC 2/NIST/HIPAA/GDPR contexts; deliver audit-ready evidence for agentic workflows. • Build privacy-first patterns (no data exfiltration by default, least-privilege tool access, secure prompt/trace storage). • Work directly with enterprise client teams to translate business processes into agentic designs; present trade-offs and proofs-of-value that lead to production. • Partner with solution leads to create domain-specific agents (e.g., RFP assist, incident RCA drafting, knowledge ops) and reusable templates.

• Design and implement AI pipelines for detection, reasoning, prioritization, and decision support • Build agent-based and workflow-driven AI systems operating over security data • Work with structured and unstructured data across logs, alerts, telemetry, and external intelligence • Collaborate on infrastructure, deployment, and scaling of AI services • Implement monitoring, evaluation metrics, and guardrails • Work closely with security domain experts to encode real-world reasoning into AI systems

• Optimize model inference using advanced techniques including quantization (GPTQ, AWQ, GGUF), distillation, pruning, and speculative decoding • Build and integrate GenAI capabilities beyond LLMs, including computer vision, image generation (Stable Diffusion, FLUX), and multimodal models • Design and implement pre-processing and post-processing pipelines, including prompt engineering, structured output parsing, guardrails, and context management • Build RAG systems, embedding pipelines, and semantic retrieval architectures for enterprise AI applications • Drive model selection, benchmarking, and cost/performance trade-off decisions across AI services • Build evaluation frameworks to measure model quality, latency, reliability, and production performance • Build production AI systems that go beyond experimentation and notebooks, focusing on scalability, reliability, and maintainability • Collaborate closely with platform, infrastructure, and product teams to deliver integrated AI services • Contribute to AI platform architecture and long-term technical direction • Participate in the full lifecycle of AI systems, from research and prototyping to production deployment and operations