Senior Python Engineer - AgenticOps

About Us

Through Fintricity, our core consulting brand, has been at the forefront of technology and digital transformation for over 19 years, most recently with a focus on bringing innovation to leading brands through expertise in big data, analytics and technologies (such as Generative AI) that are transforming operations, products, services, business models and sectors. Pivoting to a AI services and venture firm, Fintricity is working on a range of exciting new projects and ventures to build new decentralised business models, technology platforms and disrupt and transform multiple industries.

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Are you truly collaborative? Succeeding at Fintricity means respecting, understanding and trusting colleagues and clients. Challenging others and being challenged in return. Being a strong communicator, passionate, entrepreneurial and innovative about what you do. Driving yourself forward, always wanting to do things the right way. Does that sound like you?

Together. That’s how we do things. We offer a supportive, challenging and diverse working environment. We value your passion and commitment, and reward your performance.

Keen to achieve the work-life agility that you desire? We're open to discussing how this could work for you (and us).

About the Role

We are looking for a Senior Python Engineer (Full Stack) to design and build the next generation of AgenticOps platforms, enabling scalable, observable, and safe deployment of autonomous AI agents and LLM workflows.

This is a hands-on engineering role with broad responsibilities—from building backend services that orchestrate LLM pipelines, to designing web UIs that monitor, audit, and control agentic behavior. You’ll work at the intersection of Generative AI, DevOps, and full-stack development, helping to productize and operationalize intelligent agents across cloud environments.

Responsibilities

• Backend Engineering (Python)
• Build modular services for prompt orchestration, vector DB interactions, agent memory/state stores, and retrieval pipelines.
• Create scalable APIs for agent task execution, human-in-the-loop control, and logging.
• AgenticOps & LLM Integration
• Develop tools to version, deploy, monitor, and rollback LLM agents and RAG-based workflows.
• Work with cloud-based LLM APIs (OpenAI, Claude, Gemini) and open-source models (LLaMA, Mistral, etc.) via LangChain or similar.
• DevOps & Infrastructure-as-Code
• Automate deployment pipelines (Docker, K8s, Terraform), observability stacks (Prometheus, Grafana), and secure API gateways.
• Own CI/CD and testing for both code and LLM-based behavior.
• Frontend Development (React, Nextjs or Vue preferred)
• Build intuitive UIs for monitoring agents in real time, visualizing traces, showing decision logs, version history, and alerts.
• Enable role-based access, interactive prompt testing, and behavior auditing.
• Data & Observability
• Create feedback collection pipelines, behavior traces, token usage dashboards, and incident response views.
• Integrate OpenTelemetry, custom metrics, and structured logging.
• Collaboration & Leadership
• Work cross-functionally with AI scientists, DevOps engineers, and product managers.
• Provide mentorship and guide best practices in full-stack design and Pythonic architecture.

Requirements

• 5+ years in Python-based full-stack or backend development
• Solid experience with modern web frameworks (e.g., FastAPI, Django, Flask)
• Proficiency in frontend frameworks like React, Vue, or Svelte
• Strong DevOps/infra skills: Docker, Kubernetes, Terraform, GitHub Actions or similar
• Experience with LLM APIs and frameworks (LangChain, LlamaIndex, AutoGen, CrewAI)
• Familiarity with vector DBs (FAISS, Weaviate, Pinecone) and retrieval-based architectures
• Experience building monitoring/observability tooling (Grafana, Prometheus, ELK)
• Understanding of secure API design, authN/authZ, and agent sandboxing best practices
  

Nice to Haves

• Experience building agent frameworks or workflow orchestration UIs
• Familiarity with LLMOps and Responsible AI patterns (prompt auditing, guardrails, evals)
• Contributions to open-source infra or AI tooling
• Experience with serverless (e.g., AWS Lambda, Google Cloud Functions)
  

What You’ll Get

• Opportunity to shape the agent infrastructure stack of the future
• High-impact role in a cross-disciplinary team at the cutting edge of LLMs and automation
• Access to GPU/LLM infra and modern development tools
• Competitive compensation, flexible work, and continuous learning budget