Principal AI Systems Architect with 8+ years leading stateful multi-agent orchestration (LangGraph, MCP, A2A), episodic context memory with durable Redis checkpointing, resilient GraphRAG pipelines, and audit-ready governance across Johnson & Johnson, Dubizzle Dubai, and US enterprises.
Stateful agent swarm researching accounts, verifying ICP criteria, generating personalized hooks, and dispatching via API.
Deconstruct intent & validate ICP schema via Pydantic v2
↳ Allocated budget: 4 sub-agents · Enforced JSON schema contract
Model Context Protocol (MCP) lookup in enterprise CRM & SEC 10-K
↳ Extracted: revenue growth + AI hiring signals + org hierarchy
Persist durable episodic session state to Redis Cluster
↳ Key: agent:eva:session_9f2a · Zero token waste on resume
Evaluate high-confidence proposal vs. manual override threshold
↳ Deterministic policy check: Confidence 0.94 > 0.85 · Auto-cleared
A modular 2026/2027 agentic infrastructure engineered for resilience, sub-second recovery, and deterministic enterprise outcomes.
Experience real-time multi-agent assistance. Coordinate specialized sub-agents, execute MCP tools, and maintain deterministic state across complex workflows.
Coordinate market research on top 5 UAE fintech prospects and generate structured outreach drafts.
Dispatching multi-agent swarm: 1) SEC/Registry parser, 2) LinkedIn lead qualifier, 3) Pydantic validation gate. Estimated token saving: 45%.
Unite your enterprise data pipelines with agentic protocols. Connect Model Context Protocol (MCP), Redis, Vertex AI, AWS Bedrock, Databricks, and Kafka without friction.
Transform raw agent traces into clear operational metrics in seconds. Measure token spend, step latency, cache hit ratios, and guardrail pass rates in real time.
Set it, forget it. Your AI Agent tackles repetitive data pipelines, regulatory checks, and B2B integrations with automated circuit breakers and state checkpointing.
Discover how enterprise multi-agent platforms transform complex intent into audited, production-grade outcomes in four resilient stages.
Enterprise agent reliability begins with zero ambiguity. Every tool call and agent transition is governed by typed Pydantic v2 schemas that enforce strict input validation, preventing prompt injection and hallucinated parameters.
class OutreachPlan(BaseModel):
account_id: str = Field(..., description="Target CRM account UUID")
decision_makers: list[Contact] = Field(min_length=1)
value_proposition: str = Field(max_length=500)
token_budget: int = Field(default=2500, le=5000)
approval_required: bool = Field(default=True)Where advanced agentic autonomy meets defense-in-depth security—engineered to protect confidential IP and satisfy the most demanding compliance mandates.
Enterprise VPC deployments with zero data egress. Dynamic routing between cloud foundation models (Vertex AI, AWS Bedrock) and self-hosted open-weight SLMs (vLLM) to meet strict regional regulatory requirements in UAE, EU, and USA.
Every external agent action is bound by strictly typed Pydantic v2 schemas. Hallucinated parameters and unapproved external API writes are rejected at the runtime level before touching downstream databases or ERPs.
Long-running multi-agent workflows preserve their exact execution state in durable Redis clusters. If an upstream LLM API drops or a Kubernetes pod restarts, agents resume without re-spending expensive tokens.
Deep experience engineering compliant platforms across Johnson & Johnson and fintech. Automated data lineage tracking, drift detection, OpenTelemetry distributed tracing, and TruLens/Ragas evaluations create inspectable audit trails.
From Fortune 50 healthcare (Johnson & Johnson) and Dubai's largest digital marketplace to next-generation enterprise Agentic AI platforms.
Architect and deploy production-grade AI orchestration engine running stateful multi-agent workflows with LangGraph, GCP serverless, and Vertex AI.
Bachelor of Science in Computer Science (2014 — 2018)
Rigorous foundation in Distributed Algorithms, Operating Systems, Data Structures & Machine Learning.
Real enterprise platforms handling millions of events, mission-critical clinical validation, and stateful multi-agent execution in production.
Stateful multi-agent runtime on GCP with LangGraph, Redis checkpointing, MCP tool interfaces, and local SLM fallbacks.
Enterprise distributed ML experimentation framework built on PyTorch, Ray Tune, and GPU clusters, cutting multi-node experiment cycles by 60%.
Asynchronous microservices architecture and real-time event streaming across UAE's largest marketplace using Kafka, Celery, and Redis.
AI risk and compliance engines with Databricks and Snowflake, plus automated 3-statement financial modeling for fintechs.
Architectural deep-dives on stateful agent swarms, durable Redis checkpointing, and enterprise AI governance designed for 2026/2027 enterprise deployments.
A practical design for reviewable agent workflows: typed state, explicit approval interrupts, durable checkpoints, and idempotent resume paths.
A practical operating model for governing generative AI: inventory, risk tiers, evidence, change control, and monitoring tied to real system boundaries.
How durable checkpoints, explicit state, and idempotent effects make multi-step agent workflows recoverable and easier to operate.
A systems view of governed retrieval: data lineage, access-aware retrieval, citation checks, evaluation baselines, and production evidence.
Whether you need to scale stateful multi-agent swarms, cut token costs and latency by 40%+, or implement audit-ready AI governance, let's discuss your technical roadmap.
Direct 30-minute deep-dive on agentic design, state graphs, and cloud infrastructure.