We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

We are seeking a Machine Learning Engineer to join the team that builds the predictive intelligence powering the Hello Heart app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations - the systems that determine what users see, when they're nudged, and how their health trajectories are shaped.

This role demands both statistical depth and engineering proficiency - you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities
Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains.
Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results
Write production-grade Python code that is clean, tested, and built for maintainability and scale.
Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor.
Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges.
Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

We are seeking a Senior AI Engineer to join core product organization, where you will design, build, and scale next-generation AI systems powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). This role focuses on developing production-grade systems leveraging LLMs, advanced machine learning, and agent-based architectures.
You will join a team within Cyber R&D organization-leading the companys core product portfolio- while driving AI innovation and establishing engineering best practices across the domain. The team focuses on building and optimizing large-scale AI systems, including LLM-based solutions and advanced multi-agent workflows, working closely with data scientists and researchers to bring ideas into production.
The Responsibilities:
Design, build, and own end-to-end AI solutions- from data collection and preprocessing to model training, evaluation, and production deployment.
Optimize systems for performance, scalability, and reliability in production environments.
Collaborate closely with product, design, and engineering teams to identify and deliver AI-driven capabilities that address real customer needs.
Stay up to date with emerging AI/ML technologies, frameworks, and best practices, and apply them where they create real impact.
Work across the stack, contributing to backend systems and data pipelines that support large-scale AI applications.
Troubleshoot and resolve complex system issues, including performance bottlenecks, race conditions, and memory-related challenges.
Approach problems with a strong analytical mindset, delivering robust solutions while contributing to a high-performing, collaborative team environment.

We're looking for a Backend Engineer to help build and scale the Machine Learning Platform that powers how our company uses AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role at our company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities

AI/ML Architecture: Design and implement scalable AI systems using models such as
LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP)
MCP Integration: Architect and orchestrate model context flows using MCP or similar
frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend
systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and
Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a
culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets,
feedback loops, and model evaluation pipelines

As a Machine Learning Scientist II, you will work within a cross-functional team of engineers and product managers to develop, evaluate, and deploy GenAI-powered solutions for scalable, customer-facing applications. Your work will focus on implementing agentic capabilities, contributing to evaluation frameworks, and delivering measurable business impact through data-driven experimentation.


Key Job Responsibilities and Duties:

Contribute to the design and development of end-to-end agentic systems, ensuring code quality and efficiency in production.

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Adhere to and promote established best practices in GenAI application development within the team.

Collaborate actively with team members, participating in code reviews, sharing knowledge, and contributing to a positive team environment.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

As a Machine Learning Engineering Manager, you will lead a team focused on the foundational ML & Data layers to power the ranking & recommendation systems in scope. You will drive the development of robust data & ML pipelines at scale, lead the implementation of the tools for ML scientists to test and productionize advanced ML RecSys solutions.

As a technical manager of Machine Learning Engineers and Data engineers, you should be passionate about technology, keep up to date with recent breakthroughs in the field, define and shape the teams ML and platforms roadmap, and not be afraid to get your hands dirty with code when needed.

You are expected to be the focal point for all technical aspects, make sure your team members deliver on their tasks, and work together with other stakeholders to define and shape the roadmap of our products. You will work independently and will also be responsible for making technical decisions within your team.

When it comes to management, your expertise in handling people will motivate and inspire them to reach outstanding success! You should have experience in developing people. You will mentor and coach your team while working closely with a Product Manager.



Key Job Responsibilities and Duties:

Lead and develop a high-performing team, fostering individual growth and collaboration.

Manage and mentor ML engineers and Data engineers, ensuring their professional development and effectiveness.

Develop scalable ML infrastructure and pipelines for efficient data processing and evaluations deployment.

Evaluate architecture solutions based on cost, business needs, and emerging technologies.

Collaborate closely with software engineers to ensure seamless deployment and model inference.

Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.

Collaborate with stakeholders to translate business requirements into viable ML solutions.

Evaluate and integrate new ML technologies to enhance productivity and performance.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.