Senior Machine Learning Engineer

About the Role

As a Senior Machine Learning Engineer, you will collaborate closely with the Head of AI Transformation to conceptualize, develop, and maintain an internal AI platform. This role offers a wide scope that will evolve alongside your career growth within the company.

Key Responsibilities

• Construct foundational layers of the platform, including retrieval systems, knowledge access interfaces for internal data, an agent runtime and registry, and a controlled data-access layer.
• Develop and manage the deployment infrastructure delivering AI tools to business functions, including scaffolding, Continuous Integration/Continuous Deployment (CI/CD) with integrated security validations, risk-based deployments, monitoring, and rollback mechanisms.
• Create agentic applications and internal automations on the platform to enhance engineering and business workflow efficiency.
• Prioritize open standards and reusable interfaces by integrating managed or open-source components for commoditized features and focusing on building proprietary elements that provide unique advantages.
• Manage the complete lifecycle of machine learning and agent microservices — from planning and designing to implementation, deployment, and monitoring — collaborating tightly with engineering peers.
• Produce well-written, efficient, reusable, and maintainable codebases while taking accountability for legacy systems and workflows.
• Adopt a security-first and change-management mindset given the fintech environment and sensitive employee data, actively liaising with information security, product teams, data scientists, and business stakeholders to establish clear requirements.
• Effectively communicate complex technical concepts to both technical and non-technical stakeholders.
• Expand your ownership as the platform and team mature, overseeing architecture, introducing new features, and influencing technical strategies.
• Drive platform architecture and direction across various layers including authentication, integration interfaces, observability, and deployment pipelines, developing shared foundations for reuse.
• Mentor engineers, fostering a culture of engineering excellence and continuous professional development.
• Lead planning and execution of medium to large-scale projects while promoting sustainable development workflows to enhance team productivity.

Candidate Profile

• Bachelor’s degree in Computer Science or equivalent hands-on experience.
• Minimum of 7 years backend engineering experience including leadership roles.
• At least 3 years hands-on experience with Python.
• Proficiency in Flask or FastAPI frameworks, RESTful API design, SQL databases, and experience with cloud services (AWS or GCP).
• Working knowledge of message queue systems such as RabbitMQ, Kafka, or AWS SQS, containerization with Docker, and modern CI/CD pipelines.
• Experience with production-grade monitoring and observability tools like Datadog or Grafana that track latency, errors, and alerts in scalable backend environments.
• Hands-on background developing Large Language Model (LLM) and agentic applications including retrieval-augmented generation (RAG) pipelines, tool invocation, agent orchestration, and prompt engineering.
• Strong interpersonal and collaboration skills for working with cross-functional teams.
• Proven record of leading and mentoring teams, with successful delivery of medium to large software initiatives.

Beneficial Skills

• Expertise scaling chat-like applications with over 1,000 active users, addressing architecture, performance, and reliability challenges.
• Production deployment of LLM models via APIs and self-hosted options, including deep understanding of observability and agentic concepts such as memory and token/prompt caching.
• Experience streaming LLM outputs from backend to frontend using Server-Sent Events (SSE), WebSockets, or HTTP chunked transfer to provide responsive real-time user experiences.
• Familiarity with modern agent integration standards like the Model Context Protocol and construction or integration of tool and agent interfaces.
• Knowledge of vector databases such as pgvector, Pinecone, or Weaviate and embedding techniques.
• Experience with agent and LLM frameworks like LangGraph or LlamaIndex, and enterprise search or RAG stack implementations.
• Deploying machine learning models in production systems.
• Integration experience with enterprise tools and internal knowledge management systems.
• Background working in fintech or other regulated, security-conscious industries.
• Startup work experience is a plus.