AI Systems Engineer (Backend + GenAI Infrastructure) at Predictive Data Sciences
We are looking for a strong Systems Engineer with a passion for AI to build the backbone
of our agentic platform. You won’t just be identifying insights or prompting models—you
will be architecting the cloud infrastructure, state management systems, and
asynchronous runtimes that allow complex AI agents to plan, code, and execute reliability
at scale.
This is a high-impact role for a builder who understands that a great AI agent is only as good
as the system architecture running it.
What You’ll Work On
*Core Systems & Cloud Architecture (70%) *
• Build Scalable Backend Services: Design and implement robust APIs (FastAPI)
that handle multi-turn conversations, manage complex state, and persist execution
artifacts (plans, code, logs) to a permanent database layer.
• Asynchronous Agent Orchestration: Architect the communication layer between
front-end interfaces and backend agents, implementing async patterns
(WebSockets, SSE, Queues) to handle long-running tasks.
• Cloud & Security: Deploy secure, horizontally scalable infrastructure on platforms
like Vercel, Google Vertex AI, or AWS. Handle authentication, rate limiting, and
secure execution sandboxes.
• Knowledge Management Support: Build the "Knowledge Studio" backend—APIs
and storage schemas that allow users to manage prompts, documentation, and
domain concepts dynamically.
*AI Engineering & ML Ops (30%) *
• Model Orchestration: Deploy and manage Small Language Models (SLMs) on the
cloud for specialized implementation subtasks (e.g., ambiguity resolution),
optimizing for latency and cost.
• RAG & Context Engineering: Implement retrieval pipelines that dynamically fetch
the right context for the agent, ensuring high relevance and low hallucination.
• Fine-tuning & Evaluation: Manage pipelines for fine-tuning models on specific
domains (coding, tool selection) and running automated evaluations of agent
performance.
*What We’re Looking For *
*Technical Skills *
• System Design: Strong proficiency in Python (FastAPI/Django) and experience
designing schema for complex applications (PostgreSQL, NoSQL).
• Cloud Native: Experience deploying and scaling on modern cloud platforms (GCP
Vertex, AWS, Vercel) and using containerization (Docker/Kubernetes).
• AI Integration: Practical experience integrating LLMs/SLMs via APIs and
orchestrating workflows (LangChain, LlamaIndex, or custom orchestration).
• Async Patterns: Understanding of how to handle long-running jobs (Celery, Redis,
Queues) and real-time frontend updates.
*Soft Skills & Mindset *
• Engineering First: You treat AI agents as software systems that need to be tested,
debugged, and versioned.
• Versatility: You are comfortable jumping from optimizing a SQL query to finetuning
a Llama-3 model.
• Problem Structuring: You can take a high-level goal like "handle ambiguity" and
break it down into a technical workflow involving specific model calls and UI
interaction states.
*Bonus Points For *
• Experience with Vector Databases (Pinecone, Weaver, pgvector) at scale.
• Experience building Agentic Workflows (planning, tool use, reflection loops).
• Familiarity with finetuning techniques (LoRA, PEFT) for SLMs.
• Background in frontend integration (React/Next.js) to understand the full user
lifecycle.
Who Will Thrive in This Role
You’ll be a great fit if you:
• Are a software engineer first who loves AI, rather than a data scientist trying to
learn engineering.
• Want to build the engine, not just the fuel.
• Enjoy the challenge of making nondeterministic LLMs behave reliably in a
production system.