Headline

AI Engineer @ Visionet Systems Inc. | Building Enterprise Agentic Platform

About

As an AI Engineer at Visionet Systems Inc., I contribute to the development of AgentVerse, an advanced enterprise-grade AI agent platform for multi-agent orchestration. My work centers on engineering reliable production infrastructure, including agent execution loops, state persistence, and retry mechanisms, which significantly enhance operational efficiency and save engineering hours. I also built an internal LLM gateway that streamlines multi-provider integration, optimizes costs, and enforces compliance. With a B.Tech in Artificial Intelligence from Presidency University Bangalore, I focus on creating practical, scalable solutions for applied generative AI. My experience spans building LLM pipelines, designing retrieval-augmented generation (RAG) systems, and implementing observability frameworks to improve system reliability. My goal is to develop AI tools that solve complex business problems and drive measurable impact.

Location

Bengaluru

AI stack

claude code

codex

cursor

Work experience

AI Engineer at Visionet Systems Inc., Feb 2025 - Present · 1 yr 7 mos, Bengaluru, Karnataka, India, Core contributor to Visionet AgentVerse — a composable, enterprise-grade AI Agent Platform for multi-agent orchestration. • Engineered the production agent harness — the execution loop, tool sandboxing, state persistence, retry/fallback logic, and runaway-loop guards — shifting reliability from prompt-tuning to infrastructure and saving 200+ engineering hours per month. • Built an internal LLM gateway with multi-provider routing across OpenAI, Anthropic, and Gemini, semantic response caching, rate limiting, token-budget enforcement, and PII scrubbing — decoupling product teams from vendor-specific APIs and cutting LLM spend across services. • Architected RAG pipelines with hybrid retrieval, re-ranking, metadata filtering, structured outputs, and layered memory (short-term, episodic, long-term) over Pinecone and pgvector — turning unstructured enterprise data into governed, agent-callable knowledge. • Designed a low-code agentic workflow builder with human-in-the-loop approval gates, policy-aware tool routing, schema-validated outputs, and hallucination guardrails — enabling non-technical teams to ship autonomous workflows safely. • Stood up the agent observability stack with OpenTelemetry causal tracing (LangSmith, Langfuse), Ragas and LLM-as-judge evaluations, golden-set regression suites, and drift detection (MLflow, Prometheus, Grafana) — improving deployment reliability by 40% with full decision provenance for regulated environments. Stack: Python, LangChain, LangGraph, LlamaIndex, MCP, A2A, AWS Bedrock, Vertex AI, Azure AI Foundry, Pinecone, pgvector, Redis, Docker, Terraform, OpenTelemetry, Langfuse.

Co-Founder at Quanta, Mar 2024 - Present · 2 yrs 6 mos, Bengaluru, Karnataka, India, Co-founded Quanta to ship applied generative AI products that solve real business problems with measurable impact — not demos. What I own: • Set the technical direction and architecture for Quanta's GenAI product surface, from prototype to production. • Designed end-to-end LLM pipelines: retrieval, prompting, evaluation, deployment, and cost monitoring. • Built tiered inference architectures (rules → classic ML → LLM) to keep unit economics workable as usage scales. • Owned the trade-off conversations with stakeholders: when to fine-tune vs. prompt, when to call an LLM vs. avoid one, and where reliability matters more than capability. Focus: applied generative AI for productivity workflows — building tools where the model is a component, not the whole product, and where every inference request answers "can this be solved without an LLM?" before calling one. Stack: Python, LangChain, OpenAI/Anthropic APIs, vector databases, FastAPI, Docker, AWS.

Co-Founder at HUANAR, Oct 2022 - Dec 2023 · 1 yr 3 mos, Bengaluru, Karnataka, India, Co-founded HUANAR, an EdTech venture blending AI with learning and development to make personalized education accessible. What I owned: • Set product direction and tech architecture from zero — user research, MVP scoping, build, and iteration with early users. • Designed and shipped the learning experience: UI/UX, content delivery, and interaction model. • Integrated AI into the L&D loop: automated content recommendations, learner-progress signals, and personalization driven by usage data. • Wore the full early-stage hat: customer conversations, hiring early collaborators, and prioritising the smallest set of features that demonstrably moved learner outcomes. Why it matters: HUANAR was where I learned to think in user outcomes, not features. Every technical decision was filtered through one question — "does this actually help someone learn faster?" That product instinct is what I now bring to every AI system I ship. Focus: EdTech, AI-assisted learning, UX design, product-led iteration.

Software Developer at Vikasana, Mar 2022 - Jan 2023 · 11 mos, Bengaluru, Karnataka, India, Full-stack development internship building production-grade web applications and learning the discipline of shipping code that real users depend on. What I owned: • Built and shipped end-to-end web features — frontend (HTML/CSS/JavaScript) and backend (REST APIs, database integration). • Translated product requirements into technical specs and working code under real deadlines. • Debugged across the stack: from CSS layout issues to backend query performance. • Collaborated with designers, PMs, and senior engineers in a structured agile workflow — stand-ups, code reviews, sprint planning. Key takeaway: this internship gave me the engineering fundamentals — version control, testing, deployment, code review — that I now apply to every AI system I build. Production discipline starts here, not in ML libraries. Stack: HTML, CSS, JavaScript, Python, REST APIs, Git, SQL.

Education

B.tech, Artificial Intelligence, Presidency University Bangalore

Public links

github: https://github.com/zeeshanunique