Principal AI Engineer at BuzzBoard – Hyderabad | Jobyssey

Principal AI Engineer
BuzzBoard · Remote (WFH) · Engineering / AI

The role
BuzzBoard builds AI products for the B2SMB market — helping agencies, media companies, and sellers understand small businesses and market to them at a level of personalization that wasn't previously economical. Our platform is built on frontier models end to end. Not AI features bolted onto a legacy product — the products are agent systems.
Our products span multi-agent marketing content generation, real-time AI voice intake, and pre- and post-sales intelligence for SMBs — Zylo, IRIS, Ignite, and Ember. They share a common internal pipeline for orchestration, retrieval, tool use, evaluation, and deployment.
This role owns the architecture underneath that pipeline. Not one feature — the shared layer every product depends on: how retrieval is built and measured, how agents reason and hold state and fail safely, which models run where and what happens when one degrades, how output quality is evaluated before it ships, and where the line sits between what a model decides and what deterministic code decides. You'll set that architecture, build the reusable patterns products inherit, guide the engineers implementing them, and own whether it holds up in production at volume.
It's a senior individual-contributor role. Two boundaries, stated plainly:

Not a research role. The deliverable is a shipped, reliable capability, not a paper or a demo.
Not a DevOps role. You need to be fluent in deployment and able to get a service into production, but full-time platform ownership sits elsewhere. Your center of gravity is AI system design.

If your GenAI experience is mostly notebooks and proofs of concept that never carried real traffic, this is not the role.
What you'll own
AI system architecture. Design the systems behind content generation, business intelligence, recommendations, and agentic workflows — including where the boundary sits between what a model decides and what deterministic code decides. Create reusable patterns, prompts, evaluation flows, and orchestration layers that hold up across products rather than being rebuilt per feature.
Agentic workflows. Lead the design of agents that reason, call tools and APIs, manage state, checkpoint, and hand tasks off cleanly. Build the guardrails that keep them grounded and predictable — the hard part is not making an agent act, it's making it fail safely and legibly when it's wrong.
Retrieval and model strategy. Design RAG pipelines end to end: chunking, embeddings, metadata, reranking, and retrieval evaluation that actually measures whether the right context arrived. Own model selection across ecosystems on the axes that matter — cost, latency, accuracy, reliability — and define the fallback and model-switching behavior for when a provider degrades.
Evaluation and quality. Stand up the evaluation frameworks that decide whether an AI output ships: output quality, edit ratio, hallucination rate, schema adherence, latency, failure rate, inference cost. Build regression testing so a prompt or model change can't silently break production. Turn “the output feels off” into a measured threshold.
Production readiness. Partner with engineering and platform teams so systems are deployable, observable, and maintainable. Package services (Python, FastAPI/Flask, Docker) when it's the fastest path, and diagnose the production failure modes specific to AI — rate limits, cost spikes, model failures, degraded output — rather than escalating them blind.
Technical leadership. Mentor GenAI engineers, review designs and prompts and workflows and evals, and translate product requirements into architecture with measurable acceptance criteria. Communicate the tradeoffs to engineers and to leadership in language each can act on.

What we're looking for
Required

5+ years in engineering, AI, ML, or data-product work; 3+ years hands-on with GenAI / LLM systems
Production GenAI experience — non-negotiable. You have built or scaled systems that carried real traffic, and you can walk through one in detail: what broke, what it cost, what you changed.
Deep working knowledge of LLMs and SLMs: prompt engineering, structured outputs, tool and function calling
Hands-on with at least two major LLM ecosystems, and able to reason about the tradeoffs between them rather than defaulting to the one you know
RAG built for real: vector stores (Chroma, Pinecone, Weaviate, FAISS, or equivalent), embeddings, semantic search, and retrieval quality you've actually measured
Hands-on with at least one agentic framework (LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalent) and the underlying concepts — memory, state, tool integration, failure handling — well enough to move across frameworks
Strong Python; comfortable with REST APIs, Docker, a cloud platform, and basic CI/CD
Demonstrated evaluation work: regression testing, hallucination checks, schema validation, output scoring — you can define metrics for quality, reliability, cost, and business impact
Track record guiding a small team or owning AI architecture end to end
Comfort creating structure in a fast-moving environment with shifting requirements

Preferred

Fine-tuning or supervised training workflows
SLM and open-source model deployment; model serving with vLLM, Ollama, or TensorRT-LLM
Multimodal work across text, image, audio, or video
Kubernetes or serverless deployment
Evaluation/observability tooling — LangSmith, MLflow, Weights & Biases, or equivalent
Marketing technology, SMB intelligence, or content-automation domain experience
Responsible AI, privacy, security, and compliance practice
Experience scaling AI systems that generate high volumes of content, recommendations, or insights

What we care most about

You've built or scaled real GenAI systems, not just demos
You design AI workflows that are reliable, testable, and cost-aware from the start
You make practical model, prompt, retrieval, and architecture calls and can defend them
You know how to balance model intelligence against deterministic logic and engineering guardrails
You stay hands-on while guiding engineers — neither purely a manager nor purely an IC
You think past “which model” to the whole system around the model

What this role is not

Not a research or publications role
Not full-time DevOps or platform ownership
Not a role for someone whose GenAI track record is demos that never took production traffic

What we offer

Fully remote
A production GenAI foundation already carrying real volume — you scale it, you don't start from zero
Genuine architectural ownership over the agentic systems that come next
A small, high-context team that moves quickly and argues about the work
Real impact on small businesses

Apply if you've shipped GenAI systems that carried real traffic and you want to own the architecture of what comes next.

Principal AI Engineer

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