Astro Sirens is an IT staffing agency based in Austin, Texas. We connect talented professionals from around the world with U.S. companies, offering exciting opportunities to work on innovative projects for top clients. We are currently seeking a Senior Data Scientist / Machine Learning Engineer to help our clients design, build, and deploy scalable machine learning solutions that drive business value. This is a remote position, and we strongly encourage and give preference to candidates based in India who are eager to collaborate with U.S.-based teams. Responsibilities - Design, develop, and deploy machine learning models for real-world production use cases - Analyze large and complex datasets to extract insights that inform model development and optimization - Build end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment - Collaborate with data engineers, software engineers, product managers, and business stakeholders to define machine learning requirements - Implement model monitoring, performance tracking, and retraining strategies - Optimize models for scalability, performance, and reliability in cloud-based environments - Ensure data quality, reproducibility, and adherence to best practices in ML development - Translate machine learning outcomes into clear, actionable insights for technical and non-technical audiences - Contribute to improving ML standards, tools, and best practices across teams - Mentor junior data scientists and machine learning engineers

Company Description Hi! We're UW. We’re on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings! We’re aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you. Got your attention? Read on.. We put people first.. it's all about you! We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software. In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production. You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools. Job Description We Deliver Progress.. What you'll do and how you'll make an impact.. As a Machine Learning Engineer at UW, your responsibilities will include: - Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines. - Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications. - Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend. - Deployment & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance. - Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift. - Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders. Qualifications We put people first. It’s all about you.. - Technical Mastery: - Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus). - Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential. - MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana). - Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases. - Impact & Scope: - You have a track record of leading high-impact initiatives that align with company strategy. You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery. - Planning & Delivery: - You are capable of independently implementing small to medium sized features through to completion. - Operational Excellence: - Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team. - Business & Domain Knowledge: - Experience in working in a relevant consumer-centric domain - Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems - Leadership & Culture: - Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards. Skills / Competencies - Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures. - Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences. - Accountability: Willingness to take ownership of critical systems and participate in on-call rotations. - Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team. Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW. Additional Information We’ve got big ambitions so there’s going to be plenty of challenges. There are also a lot of benefits: - An industry benchmarked salary. We’ll share it during your first conversation. - Discretionary performance bonus - Flexible remote working with the option to visit our London hubs for team meetings and company events. You'll have access to a range of hot desking spaces around the UK - Electric Car salary sacrifice scheme through Tusker - Discount on our services and you get our coveted Cashback Card for free. You’ll also get access to 100s of rewards and discounts through Perkbox - A matched contribution pension scheme and life assurance up to 4x your salary. You can also access free mortgage advice and a financial wellbeing tool. - Family-friendly policies, designed to help you and your family thrive. - Discounted private health insurance, access to an Employee Assistance line and a free Virtual GP. Our wellbeing app Unmind supports your mental health. - Belonging groups that help UW shape an even more inclusive future. - A commitment to helping you develop your career journey through learning, coaching and new experiences Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do. We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered. Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment. - Employment Status: Full-Time

Role Description We are looking for a Machine Learning Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include: - Creating machine learning models and retraining systems. - Studying and transforming data science prototypes. - Designing machine learning systems. - Researching and implementing appropriate ML algorithms and tools. - Developing machine learning applications according to requirements. - Selecting appropriate datasets and data representation methods. - Running machine learning tests and experiments. - Performing statistical analysis and fine-tuning using test results. - Training and retraining systems when necessary. - Extending existing ML libraries and frameworks. - Keeping abreast of developments in the field. Qualifications - Proven experience as a Machine Learning Engineer or similar role. - Understanding of data structures, data modelling and software architecture. - Deep knowledge of math, probability, statistics and algorithms. - Ability to write robust code in Python, Java and R. - Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn). - Excellent communication skills. - Ability to work in a team. - Outstanding analytical and problem-solving skills. - BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus. Company Description

Who Is Nisos? Nisos is the human risk management company specializing in unmasking threats before they escalate. We are a trusted advisor who operates as an extension of security, intelligence, legal, and human resource teams to protect their people and business. Our intelligence-led solutions help enterprises make critical decisions, manage human risk, and drive real world consequences for digital threats. For more information, visit: https://www.nisos.com. What Is The Opportunity? Join an AI/ML team designing and shipping production ML systems that combine Large Language Models (LLMs), retrieval-augmented generation (RAG), and modern agent frameworks for multi-step decision making. You will help turn massive, noisy data into actionable intelligence for analysts and customers. What Are The Responsibilities? - Integrate and fine-tune LLMs for natural-language understanding, reasoning, and multi-step tool orchestration. - Build agentic workflows (e.g. using LangGraph) to coordinate complex tasks across data sources and services. - Architect and implement AI/ML features for pattern recognition, entity extraction, and automated intelligence gathering at scale. - Develop scalable data pipelines and streaming architectures that process billions of heterogeneous data points. - Create and optimize vector databases and retrieval systems for semantic search and RAG. - Deliver production-ready APIs and microservices supporting real-time analysis and model inference. - Own model deployment and monitoring using modern ML/LLMOps practices; drive continuous improvement in accuracy, latency, and cost. - Evaluate LLM systems with offline metrics, golden test sets, prompt A/B tests, latency & cost optimisation. - Collaborate with analysts and product teams to translate intelligence needs into robust ML solutions. Who You Are - Expert proficiency in Python, with experience of at least one of deep learning frameworks, PyTorch and TensorFlow - Hands-on with LangChain, LangGraph, AutoGen or similar agent LLM frameworks - Deep experience with prompt engineering, foundation model API integration and LLM finetuning - Expertise in RAG systems with vector databases (Pinecone, Weaviate) and embedding technologies - Strong experience with LLMOps practices for observability and traceability and LLM evaluation. - Deep understanding of distributed systems, microservices, and real-time streaming architectures - Strong containerization and infrastructure skills (Docker, ECS, Terraform, CloudFormation) - Experience building production AI systems with strong safety, privacy, and ethical practices - Familiarity with computer vision and NLP techniques for OSINT - Understanding of adversarial AI and security implications Required Education and Experience - Degree in Computer Science, AI/ML, or equivalent experience - Substantive experience in building AI/ML products at scale with proven experience in LLMs, RAGs or Agentic AI solutions - Proven experience shipping production AI systems at scale - Track record of building systems that process and analyze large-scale unstructured data - Experience in security, intelligence, or OSINT platforms preferred Where will you work? - Hybrid office schedule: employees balance working remotely and working at our office in Belfast City Centre. - Flexible schedule: employees generally work normal business hours. We routinely meet in the afternoon with US-based colleagues, but we rarely schedule meetings earlier than 8 am or past 6 pm local time. What equipment will you use? The position requires use of Macbooks and MacOS. We ensure you can work well from home by providing Macbook Pros, monitors, peripherals, and noise-canceling headphones.