AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations—including banks, financial institutions, and large enterprises—to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors. Role Overview As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You’ll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities Model Evaluation & Customization:Evaluate, fine-tune, and implement appropriate AI/ML models on AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints.Architectural Assessment:Assess and recommend various model architectures, ensuring that selected solutions meet the high standards required by complex business problems in financial services and other regulated industries.Enterprise Integration:Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards.Advanced AI Techniques:Drive the development and implementation of state-of-the-art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications.Specialization & Innovation:Take ownership of specialized areas within machine learning/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions.Collaboration & Quality Assurance:Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to roll out features, manage quality assurance, and ensure that all deployed models meet both performance and compliance benchmarks.Documentation & Compliance:Create and maintain detailed technical and product documentation, with an emphasis on auditability and adherence to regulatory standards. Qualifications Educational & Professional Background:A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors.Regulated Industry Experience (FS, Banking or Insurance is preferred)Proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings.Technical Expertise:Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.).Diverse Data Handling:Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise-level datasets.Deployment Proficiency:Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems.Publications & Contributions:Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded.

AryaXAI is looking for a passionate and capable engineer to lead the development of our Enterprise AI Agent and AI Data Science Agent workstreams. Our mission is to build agentic systems that bring explainability, safety, and scalability to the forefront of enterprise AI. As an AI Agent Engineer at AryaXAI, you'll play a key role in shaping intelligent agents that assist in enterprise decision-making, automate complex data science workflows, and integrate seamlessly into business ecosystems. These agents will serve as collaborators for enterprise teams—leveraging reasoning, tool use, and memory—while staying aligned with our focus on transparent and trustworthy AI. We're seeking a strong engineering generalist with deep experience in LLMs, agent frameworks, and production-scale systems. You’ve likely prototyped or shipped agent-based systems, pushed the boundaries of what LLMs can do, and are looking for a meaningful opportunity to build the future of responsible AI. Responsibilities Lead the design and development of Enterprise AI Agents and Data Science Agent systems that combine reasoning, tool orchestration, and memory. Collaborate with product, research, and infrastructure teams to create scalable agent architectures tailored for enterprise users. Build agent capabilities for tasks like automated analysis, reporting, data wrangling, and domain-specific workflows across business verticals. Integrate real-time knowledge, enterprise APIs, RAG pipelines, and proprietary tools into agentic workflows. Work closely with the alignment and explainability teams to ensure agents remain safe, auditable, and transparent in their reasoning and output. Continuously evaluate and incorporate advances in GenAI (e.g., controllability, multi-modal models, memory layers) into the agent stack. Help shape the engineering culture at AryaXAI through mentorship, reviews, and innovation. Requirements Demonstrated experience building with LLMs and Agentic frameworks (e.g., LangChain, LangFlow, Semantic Kernel, Haystack, ReAct, AutoGPT, etc.). Experience with productionizing AI/LLM workflows and integrating them into real-world applications or systems. 2+ years of software engineering experience, ideally with some time in early-stage startups or AI-first environments. Strong Python skills and solid understanding of full-stack backend architecture (APIs, cloud infra, databases). A passion for building ethical, explainable, and high-impact AI products that solve real enterprise problems. (Bonus) Experience working on agent toolchains for data science, ML ops, or enterprise analytics. Show more Show less

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations—including banks, financial institutions, and large enterprises—to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors. Role Overview As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You’ll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities Model Evaluation & Customization: Evaluate, fine-tune, and implement appropriate AI/ML models on AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints. Architectural Assessment: Assess and recommend various model architectures, ensuring that selected solutions meet the high standards required by complex business problems in financial services and other regulated industries. Enterprise Integration: Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards. Advanced AI Techniques: Drive the development and implementation of state-of-the-art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications. Specialization & Innovation: Take ownership of specialized areas within machine learning/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions. Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to roll out features, manage quality assurance, and ensure that all deployed models meet both performance and compliance benchmarks. Documentation & Compliance: Create and maintain detailed technical and product documentation, with an emphasis on auditability and adherence to regulatory standards. Qualifications Educational & Professional Background: A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors. Regulated Industry Experience (FS, Banking or Insurance is preferred) Proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings. Technical Expertise: Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.). Diverse Data Handling: Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise-level datasets. Deployment Proficiency: Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems. Publications & Contributions: Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded. Show more Show less

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. As an AI Engineer at AryaXAI, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You'll be working directly with enterprise use cases that matters. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability. Responsibilities Evaluate and fine-tune appropriate AI/ML models on AryaXAI.com, based on specific business use cases, considering accuracy, computational requirements, and scalability. Assess various model architectures to determine the most suitable solutions for different business problems Implement and deploy AI models in production environments using AryaXAI.com, ensuring seamless integration with existing systems You'll work on advanced AI architectures using state-of-the-art AI explainability, AI safety, and AI alignment techniques You'll have flexibility in picking up the specialization areas within ML/DL and problem types that address the above challenges. Collaborate with MLEs and SDE to roll out the features and manage their quality until they are fully stable. Create and maintain technical and product documentation. Qualifications Has a solid academic background in concepts of machine learning, deep learning, or reinforcement learning. Published peer-reviewed papers or contributions to open-source tools are a plus. Hands-on experience in working with deep learning frameworks like Tensorflow, Pytorch etc Enjoys working on various DL problems that involve using different types of training data sets - textual, tabular, categorical, images etc Comfortable deploying code in cloud environments/on-premise environments. 2+ years of hands-on experience in Deep Learning or Machine Learning Hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc. Show more Show less

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. As a research scientist at AryaXAI, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability. Responsibilities You'll work on advanced problems related to AI explainability, AI safety, and AI alignment. You'll have flexibility in picking up the specialization areas within ML/DL and problem types that address the above challenges. Create new techniques around ML Observability & Alignment. Collaborate with MLEs and SDE to roll out the features and manage their quality until they are fully stable. Create and maintain technical and product documentation. Publish papers in open forums like arxiv and present in industry forums like ICLR NeurIPS etc. Qualifications Has a solid academic background in concepts of machine learning or deep learning or reinforcement learning. Master or Ph.D in key engineering topics like computer science or Mathematics is required Should have published peer-reviewed papers or contributed to opensource tools Hands-on experience in working with deep learning frameworks like Tensorflow, Pytorch etc Enjoys working on various DL problems that involve using different types of training data sets - textual, tabular, categorical, images etc Comfortable deploying code in cloud environments/on-premise environments. Good fundamentals in MLOps and productionising ML models. Prior experience on working on ML explainability methods - LRP, SHAPE, LIME, IG, CEM etc. 2+ years of hands-on experience in Deep Learning or Machine Learning. Hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc. Show more Show less

We are seeking an experienced DevOps Engineer with strong Python development skills to join our team. In this role, you will be responsible for designing, implementing, and maintaining our cloud infrastructure, developing Python-based servers, and ensuring scalability across a multi-cloud environment. You will play a key role in developing scalable serverless solutions, automating deployment pipelines, and managing cloud environments (AWS, Azure, GCP). Working closely with both the development and operations teams, you will ensure the seamless integration, deployment, and maintenance of our PaaS/IaaS platform with 99% uptime. Key Responsibilities Design, develop, and maintain Custom Built Serverless systems for high-volume traffic and real-time processing. Architect, implement, and manage cloud infrastructure across AWS, Azure, and GCP for scalability and performance. Implement and manage container orchestration systems such as Kubernetes and ECS, ensuring optimal performance across cloud environments. Develop and maintain Infrastructure as Code (IaC) using tools like Terraform, CloudFormation, or Pulumi. Experience with building Serverless services using Batch and Jobs. Build, maintain, and optimize CI/CD pipelines for automated deployment and scaling of both serverless and containerized applications. Implement robust monitoring, logging, and alerting systems to ensure high availability, fault tolerance, and disaster recovery. Optimize cloud resource utilization for cost-effectiveness and performance improvements. Collaborate with development teams to integrate and manage APIs, microservices, and databases (SQL/NoSQL) in scalable environments. Ensure the security and compliance of our infrastructure and applications, applying cloud-native security best practices. Troubleshoot and resolve complex issues in both development and production environments. Stay current with industry trends and emerging technologies, especially in cloud, DevOps, and Python development. Contribute to open-source projects and represent the company at industry events and conferences. Required Qualifications Bachelor's degree in Computer Science, Engineering, or a related field. 3+ years of experience in DevOps, Cloud Engineering, or Python Development roles. Expertise in cloud platforms (AWS, Azure, GCP), with strong knowledge of their services, particularly for serverless, compute, and storage solutions. Proficient in Python development, with experience in building and maintaining Python servers and APIs. Advanced understanding of containerization (Docker) and orchestration systems (Kubernetes). Strong hands-on experience with Infrastructure as Code (Terraform, CloudFormation, Pulumi) and automation scripting. Proficiency in building, managing, and optimizing CI/CD pipelines using tools such as Jenkins, GitLab CI, or GitHub Actions. Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK stack) for troubleshooting and system insights. Solid understanding of cloud security, networking concepts, and security best practices in cloud environments. Excellent problem-solving skills, attention to detail, and ability to thrive in a fast-paced environment. Preferred Qualifications Experience with multi-cloud management and migration strategies across AWS, Azure, and GCP. Expertise in event-driven architectures, message queues, and serverless platforms (AWS Lambda, Google Cloud Functions, Azure Functions). Familiarity with microservices architecture, distributed systems, and databases (SQL, NoSQL) in cloud environments. Contributions to open-source projects related to cloud, DevOps, or Python development. Relevant cloud certifications (AWS, Azure, GCP). Experience in presenting at technology conferences or writing technical blogs. What We Offer Opportunity to work on and shape cutting-edge cloud and serverless technologies A collaborative and innovative work environment that encourages creativity and pushes technological boundaries Competitive salary and benefits package Professional development and learning opportunities Remote work options Chance to contribute to the broader tech community through open-source contributions and conference presentations Show more Show less

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. Qualifications This is a full-time remote role for a Principal Engineering Manager at AryaXAI. The Principal Engineering Manager will be responsible for engineering management, team leadership, software development, project management, and integration. 7+ years of experience in building and deploying enterprise-grade machine learning infra, SaaS software or data analytics solutions or online platforms Academic degree in Computer Science, Engineering, or a related field Strong background in Python programming, Distributed computing, Engineering, big data processing, and cloud computing (AWS, Azure, GCP, On-Premise) Experience in optimizing, scaling, and reliability of large-scale systems for various data modalities and AI models. Strong fundamentals and discipline in CI/CD pipelines, containerization (Docker, Kubernetes), and multi-cloud environments. Proven track record in product management in designing and delivering complex, high-performance solutions for large-scale enterprise customers without faults and ability to auto-scale the systems through delivering the essential SLAs in production during inferencing. Programming skills - expert in Python and solid coding practices Data Engineering - expert in MongoDB and strong fundamentals in data lakes, data storage, ETL pipelines, and building real-time data pipelines. Strong material skills, excellent communication, collaboration, and leadership skills with the ability to lead teams, mentor juniors, and work with other teams. Strong system architecture background that supports scaling in regulated industries. Experience in scaling AI/ML production systems using classic ML models, LLMs, and Deep Learning models on traditional hardware and high-performance computing. Roles And Responsibilities As Principal Engineer, you'll be the main SPOC for all the engineering efforts behind the AryaXAI platform, serving customers in SaaS and on-premise modes and scaling multiple AI models in startups, SMEs, and highly regulated industries. You'll be responsible for designing, modifying, and scaling architecture, as well as designing and implementing scalable infra for AI/ML solutions and data engineering. You'll be the SPOC with the R&D team collecting the productized components and adding and scaling these components on the platform. Architect and scale AI inferencing platform and related components for all data modalities like Tabular, text, image, video, etc. You'll be responsible for designing the AI Ops and AI Inferencing Ops - model building, data preprocessing, model inferencing, explainability, monitoring, alignment, risk management components. You'll work on running multiple optimizations on inferencing speed and latencies to serve multiple AI models - LLMs, DL models, classic ML models, etc. You'll be the SPOC with the pre-sales and solutions team to provide necessary documentation and specs and collect the requirements for enterprise deployments. You'll manage product development activities like scrum, reviewing the scrums, and optimizing productivity across teams - engineering, data science, and front end. You'll mentor and guide juniors and middle managers. You'll continuously improve the standards of the product, as well as the architecture, culture, and experience. What You’ll Get Highly competitive and meaningful compensation package One of the best health care plans that covers not only you but also your family A great team Micro-entrepreneurial tasks and responsibilities. Career development and leadership opportunities Show more Show less

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations—including banks, financial institutions, and large enterprises—to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors. Role Overview As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You’ll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities Model Evaluation & Customization: Evaluate, fine-tune, and implement appropriate AI/ML models on AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints. Architectural Assessment: Assess and recommend various model architectures, ensuring that selected solutions meet the high standards required by complex business problems in financial services and other regulated industries. Enterprise Integration: Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards. Advanced AI Techniques: Drive the development and implementation of state-of-the-art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications. Specialization & Innovation: Take ownership of specialized areas within machine learning/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions. Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to roll out features, manage quality assurance, and ensure that all deployed models meet both performance and compliance benchmarks. Documentation & Compliance: Create and maintain detailed technical and product documentation, with an emphasis on auditability and adherence to regulatory standards. Qualifications Educational & Professional Background: A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors. Regulated Industry Experience (FS, Banking or Insurance is preferred) Proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings. Technical Expertise: Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.). Diverse Data Handling: Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise-level datasets. Deployment Proficiency: Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems. Publications & Contributions: Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded. Show more Show less

At AryaXAI , we’re building the future of explainable, scalable, and aligned AI —designed specifically for high-stakes environments where trust, transparency, and performance are non-negotiable. From financial services to energy and other regulated industries, our platform powers intelligent decision-making through safe and robust AI systems. We’re looking for a Data Scientist with a deep understanding of both classical and deep learning techniques, experience building enterprise-scale ML pipelines, and the ambition to tackle real-world, high-impact problems. You will work at the intersection of modeling, infrastructure, and regulatory alignment—fine-tuning models that must be auditable, performant, and production-ready. Responsibilities: Modeling & AI Development Design, build, and fine-tune machine learning models (both classical and deep learning) for complex mission-critical use cases in domains like banking, finance, energy, etc. Work on supervised, unsupervised, and semi-supervised learning problems using structured, unstructured, and time-series data. Fine-tune foundation models for specialized use cases requiring high interpretability and performance. Platform Integration Develop and deploy models on AryaXAI’s platform to serve real-time or batch inference needs. Leverage explainability tools (e.g., DLBacktrace, SHAP, LIME, or AryaXAI’s native xai_evals stack) to ensure transparency and regulatory compliance. Design pipelines for data ingestion, transformation, model training, evaluation, and deployment using MLOps best practices. Enterprise AI Architecture Collaborate with product and engineering teams to implement scalable and compliant ML pipelines across cloud and hybrid environments. Contribute to designing secure, modular AI workflows that meet enterprise needs—latency, throughput, auditability, and policy constraints. Ensure models meet strict regulatory and ethical requirements (e.g., bias mitigation, traceability, explainability). Requirements : 3+ years of experience building ML systems in production, ideally in regulated or enterprise environments. Strong proficiency in Python , with experience in libraries like scikit-learn, XGBoost, PyTorch, TensorFlow , or similar. Experience with end-to-end model lifecycle : from data preprocessing and feature engineering to deployment and monitoring. Deep understanding of enterprise ML architecture —model versioning, reproducibility, CI/CD for ML, and governance. Experience working with regulatory, audit, or safety constraints in data science or ML systems. Familiarity with ML Ops tools (MLflow, SageMaker, Vertex AI, etc.) and cloud platforms (AWS, Azure, GCP). Strong communication skills and an ability to translate technical outcomes into business impact. Bonus Points For Prior experience in regulated industries : banking, insurance, energy, or critical infrastructure. Experience with time-series modeling , anomaly detection, underwriting, fraud detection or risk scoring systems. Knowledge of RAG architectures , generative AI , or foundation model fine-tuning . Exposure to privacy-preserving ML , model monitoring , and bias mitigation frameworks. What You’ll Get Competitive compensation with performance-based upside Comprehensive health coverage for you and your family Opportunity to work on mission-critical AI systems where your models drive real-world decisions Ownership of core components in a platform used by top-tier enterprises Career growth in a fast-paced, high-impact startup environment Remote-first, collaborative, and high-performance team culture If you’re excited to build data science solutions that truly matter , especially in the most demanding industries, we want to hear from you.

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations—including banks, financial institutions, and large enterprises—to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors. Role Overview As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You’ll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities Model Evaluation & Customization: Evaluate, fine-tune, and implement appropriate AI/ML models on AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints. Architectural Assessment: Assess and recommend various model architectures, ensuring that selected solutions meet the high standards required by complex business problems in financial services and other regulated industries. Enterprise Integration: Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards. Advanced AI Techniques: Drive the development and implementation of state-of-the-art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications. Specialization & Innovation: Take ownership of specialized areas within machine learning/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions. Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to roll out features, manage quality assurance, and ensure that all deployed models meet both performance and compliance benchmarks. Documentation & Compliance: Create and maintain detailed technical and product documentation, with an emphasis on auditability and adherence to regulatory standards. Qualifications Educational & Professional Background: A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors. Regulated Industry Experience (FS, Banking or Insurance is preferred) Proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings. Technical Expertise: Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.). Diverse Data Handling: Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise-level datasets. Deployment Proficiency: Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems. Publications & Contributions: Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded.

Are you a Python expert who has built scalable software solutions? Do you know how to handle heavy volumes of data and process the responses as APIs? Have you worked in scaling AI models or across MLE process? AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. We are looking for Sr Software Engineers who - Expert in Python and have built scalable systems. Has experience with Puppet/Chef/Ansible, Amazon Web Services (AWS), Git, Graphite and related tools for large-scale systems management is a must. Should be able to write python, bash, c/c++ and perl scripts. Good understanding of architecture and tradeoffs in distributed computing environments. Experience working with linux system monitoring and analysis. Should be hands on Cloud like aws ec, elb, s3 bucket, auto scale etc. Better understanding of nginx/Apache web server as reverse proxy , load balancer, caching. H/w, S/w load balancer. Experience with web infrastructure, distributed systems, or component-oriented software engineering What You'll Be Doing Design and implementation of the network that supports for easy deployments of the servers Building exciting new features that will enable developers to build highly available, robust AI services Maintaining and ensuring a high standard for reliability and availability across multiple datacenters Evolving our existing architecture and codebases to support building flexible networking capabilities both internally and for the product Desired Skills Very good knowledge in nosql - mongodb sharding, clustering , replication , security. Knowledge of the different kinds of databases – relational, document, key/value, graph Demonstrated open-source contribution Hand on experience in working in one or major MLE processes like data prep, model serving, compute optimizatons etc. Knowledge of TCP/IP and network programming or developing/designing large software systems What’ll You Get Highly competitive and meaningful compensation package One of the best health care plans that covers not only you but also your family A great team Micro-entrepreneurial tasks and responsibilities. Career development and leadership opportunities

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical businesses by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. As an AI Engineer at AryaXAI, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You'll be working directly with enterprise use cases that matters. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability. Responsibilities Evaluate and fine-tune appropriate AI/ML models on AryaXAI.com, based on specific business use cases, considering accuracy, computational requirements, and scalability. Assess various model architectures to determine the most suitable solutions for different business problems Implement and deploy AI models in production environments using AryaXAI.com, ensuring seamless integration with existing systems You'll work on advanced AI architectures using state-of-the-art AI explainability, AI safety, and AI alignment techniques You'll have flexibility in picking up the specialization areas within ML/DL and problem types that address the above challenges. Collaborate with MLEs and SDE to roll out the features and manage their quality until they are fully stable. Create and maintain technical and product documentation. Qualifications Has a solid academic background in concepts of machine learning, deep learning, or reinforcement learning. Published peer-reviewed papers or contributions to open-source tools are a plus. Hands-on experience in working with deep learning frameworks like Tensorflow, Pytorch etc Enjoys working on various DL problems that involve using different types of training data sets - textual, tabular, categorical, images etc Comfortable deploying code in cloud environments/on-premise environments. 2+ years of hands-on experience in Deep Learning or Machine Learning Hands-on experience in implementing techniques like Transformer models, GANs, Deep Learning, etc.

AI Research Intern – AryaXAI AI Alignment Labs Location: Remote / Mumbai, India Commitment: Full-time internship (6 months; potential extension or full-time offer) Start Date: Rolling About AryaXAI AI Alignment Labs AryaXAI AI Alignment Labs, based out of Mumbai, India and Paris, France, is the alignment and explainability division of AryaXAI.com , part of Aurionpro Solutions. We work on AI interpretability and trustworthiness for mission-critical sectors. Our open-source initiatives include the xai_evals benchmarking suite and the DLBacktrace explainability framework, both designed to make AI more transparent, reliable, and aligned with human values. What You’ll Do Collaborate closely with our research and engineering teams on one of the areas: Library Development: Architect and enhance open-source Python tooling for alignment, explainability, uncertainty quantification, robustness, and machine unlearning. Model Benchmarking: Conduct rigorous evaluations of LLMs and deep networks under domain shifts, adversarial conditions, and regulatory constraints. Explainability & Trust: Design and implement XAI techniques (LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities. Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors. Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models. Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions. General Required Qualifications Strong Python expertise: writing clean, modular, and testable code. Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch. Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc. Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices. Collaborative mindset: excellent communication, peer code reviews, and agile teamwork. Preferred Domain Expertise (Any one of these is good) : Explainability: applied experience with XAI methods such as SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM. Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection. Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation. Quantization & pruning: applying model compression to optimize size, latency, and memory footprint. LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning. Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization. Additional Experience (Nice-to-Have) Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience. Open-source contributions: prior work on AI/ML libraries or tooling. Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields. Performance optimization: familiarity with large-scale training infrastructures. What We Offer Real-world impact: address high-stakes AI challenges in regulated industries. Compute resources: access to GPUs, cloud credits, and proprietary models. Competitive stipend: with potential for full-time conversion. Authorship opportunities: co-authorship on papers, technical reports, and conference submissions.

AI Research Intern – AryaXAI AI Alignment Labs Commitment: Full-time internship (6 months; potential extension or full-time offer) Start Date: Rolling About AryaXAI AI Alignment Labs AryaXAI AI Alignment Labs, based out of Mumbai, India and Paris, France, is the alignment and explainability division of AryaXAI.com, part of Aurionpro Solutions. We work on AI interpretability and trustworthiness for mission-critical sectors. Our open-source initiatives include the xai_evals benchmarking suite and the DLBacktrace explainability framework, both designed to make AI more transparent, reliable, and aligned with human values. What You’ll Do Collaborate closely with our research and engineering teams on one of the areas: Library Development: Architect and enhance open-source Python tooling for alignment, explainability, uncertainty quantification, robustness, and machine unlearning. Model Benchmarking: Conduct rigorous evaluations of LLMs and deep networks under domain shifts, adversarial conditions, and regulatory constraints. Explainability & Trust: Design and implement XAI techniques (LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities. Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors. Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models. Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions. General Required Qualifications Strong Python expertise: writing clean, modular, and testable code. Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch. Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc. Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices. Collaborative mindset: excellent communication, peer code reviews, and agile teamwork. Preferred Domain Expertise (Any one of these is good) : Explainability: applied experience with XAI methods such as SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM. Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection. Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation. Quantization & pruning: applying model compression to optimize size, latency, and memory footprint. LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning. Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization. Additional Experience (Nice-to-Have) Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience. Open-source contributions: prior work on AI/ML libraries or tooling. Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields. Performance optimization: familiarity with large-scale training infrastructures. What We Offer Real-world impact: address high-stakes AI challenges in regulated industries. Compute resources: access to GPUs, cloud credits, and proprietary models. Competitive stipend: with potential for full-time conversion. Authorship opportunities: co-authorship on papers, technical reports, and conference submissions.

As a Senior Systems Engineer, you are the architect and guardian of the foundational infrastructure upon which the entire AryaXAI platform is built. While our Software Engineers build the applications, you build the world-class, multi-cloud environment where those applications run. You will be responsible for the design, implementation, and operational excellence of our Kubernetes clusters, core networking, and operating systems. Your role involves deep, hands-on work with the internals of Linux, Kubernetes, and cloud provider primitives to ensure our platform is secure, performant, and scales seamlessly. You will be the final escalation point for the most complex infrastructure issues and will be instrumental in building a resilient, automated, and cost-effective foundation for our AI workloads. Key Responsibilities Architect Multi-Cloud Kubernetes Infrastructure: Design, build, and maintain highly available, scalable, and secure Kubernetes clusters across multiple cloud providers (AWS, GCP). Automate Everything with IaC: Lead the development of our Infrastructure as Code (IaC) strategy using Terraform. Write clean, modular, and maintainable code to automate the provisioning of all infrastructure resources. Deep System & Network Optimization: Dive deep into the stack to optimize performance. This includes Linux kernel tuning, network stack optimization, and configuring our Kubernetes networking (CNI) for high-throughput, low-latency communication required by ML workloads. Infrastructure Security and Hardening: Define and implement security best practices for our infrastructure. This includes hardening base OS images, configuring network security policies, managing IAM roles, and ensuring the security of our Kubernetes control plane. Build Core Observability and Reliability: Engineer and manage the foundational observability stack (e.g., Prometheus, Grafana, Loki) that provides deep insight into the health and performance of our systems. Develop CI/CD Infrastructure: Build and maintain the underlying infrastructure that powers our CI/CD pipelines, enabling our software engineers to ship code rapidly and reliably. Troubleshoot Complex System Issues: Act as the subject matter expert for diagnosing and resolving complex, system-level issues that span across networking, operating systems, and the Kubernetes platform. Required Qualifications Extensive Systems Experience: 5+ years of professional experience in a Systems Engineering, Site Reliability Engineering (SRE), or a similar infrastructure-focused role. Expert-Level Linux/Unix Administration: A deep, fundamental understanding of Linux internals, including the kernel, system calls, filesystems, memory management, and the networking stack. Kubernetes Mastery: Proven, hands-on experience building and managing production-grade Kubernetes clusters from the ground up. You must have a deep understanding of the Kubernetes control plane (etcd, kube-apiserver, scheduler), worker node components (kubelet, kube-proxy), and key extension points like CNI and CSI. Proficiency in Infrastructure as Code (IaC): Expert-level skills in Terraform, with experience building complex, modular, and reusable infrastructure definitions. Advanced Networking Knowledge: A strong command of TCP/IP networking fundamentals (L3/L4), DNS, HTTP/S, and advanced cloud networking concepts including VPCs, subnets, routing, and load balancing across multiple cloud providers. Automation and Scripting: Strong scripting skills in a language like Python or Go, used for automating infrastructure management tasks. Strong hands-on experience with Infrastructure as Code (Terraform, CloudFormation, Pulumi) and automation scripting. Proficiency in building, managing, and optimizing CI/CD pipelines using tools such as Jenkins, GitLab CI, or GitHub Actions. Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK stack) for troubleshooting and system insights. Solid understanding of cloud security, networking concepts, and security best practices in cloud environments. Preferred Qualifications Multi-Cloud Operations: Demonstrable experience managing production infrastructure that spans both AWS and GCP. Advanced Kubernetes and Cloud Native Tooling: Experience with service meshes (e.g., Istio, Linkerd), policy engines (e.g., OPA/Gatekeeper), and cluster lifecycle management tools. High-Performance Networking: Experience with advanced networking solutions like Cilium with eBPF to optimize performance and security for demanding workloads. GPU and Specialized Hardware Management: Experience managing infrastructure with specialized hardware like GPUs, including setting up drivers and device plugins in a Kubernetes environment. Building Bare Metal or Hybrid Clouds: Experience managing physical servers and integrating them into a hybrid cloud environment. What We Offer Opportunity to work on and shape cutting-edge cloud and serverless technologies A collaborative and innovative work environment that encourages creativity and pushes technological boundaries Competitive salary and benefits package Professional development and learning opportunities Remote work options Chance to contribute to the broader tech community through open-source contributions and conference presentations

We are seeking an experienced DevOps Engineer with strong Python development skills to join our team. In this role, you will be responsible for designing, implementing, and maintaining our cloud infrastructure, developing Python-based servers, and ensuring scalability across a multi-cloud environment. You will play a key role in developing scalable serverless solutions, automating deployment pipelines, and managing cloud environments (AWS, Azure, GCP). Working closely with both the development and operations teams, you will ensure the seamless integration, deployment, and maintenance of our PaaS/IaaS platform with 99% uptime. Key Responsibilities Design, develop, and maintain Custom Built Serverless systems for high-volume traffic and real-time processing. Architect, implement, and manage cloud infrastructure across AWS, Azure, and GCP for scalability and performance. Implement and manage container orchestration systems such as Kubernetes and ECS, ensuring optimal performance across cloud environments. Develop and maintain Infrastructure as Code (IaC) using tools like Terraform, CloudFormation, or Pulumi. Experience with building Serverless services using Batch and Jobs. Build, maintain, and optimize CI/CD pipelines for automated deployment and scaling of both serverless and containerized applications. Implement robust monitoring, logging, and alerting systems to ensure high availability, fault tolerance, and disaster recovery. Optimize cloud resource utilization for cost-effectiveness and performance improvements. Collaborate with development teams to integrate and manage APIs, microservices, and databases (SQL/NoSQL) in scalable environments. Ensure the security and compliance of our infrastructure and applications, applying cloud-native security best practices. Troubleshoot and resolve complex issues in both development and production environments. Stay current with industry trends and emerging technologies, especially in cloud, DevOps, and Python development. Contribute to open-source projects and represent the company at industry events and conferences. Required Qualifications Bachelor's degree in Computer Science, Engineering, or a related field. 3+ years of experience in DevOps, Cloud Engineering, or Python Development roles. Expertise in cloud platforms (AWS, Azure, GCP), with strong knowledge of their services, particularly for serverless, compute, and storage solutions. Proficient in Python development, with experience in building and maintaining Python servers and APIs. Advanced understanding of containerization (Docker) and orchestration systems (Kubernetes). Strong hands-on experience with Infrastructure as Code (Terraform, CloudFormation, Pulumi) and automation scripting. Proficiency in building, managing, and optimizing CI/CD pipelines using tools such as Jenkins, GitLab CI, or GitHub Actions. Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK stack) for troubleshooting and system insights. Solid understanding of cloud security, networking concepts, and security best practices in cloud environments. Excellent problem-solving skills, attention to detail, and ability to thrive in a fast-paced environment. Preferred Qualifications Experience with multi-cloud management and migration strategies across AWS, Azure, and GCP. Expertise in event-driven architectures, message queues, and serverless platforms (AWS Lambda, Google Cloud Functions, Azure Functions). Familiarity with microservices architecture, distributed systems, and databases (SQL, NoSQL) in cloud environments. Contributions to open-source projects related to cloud, DevOps, or Python development. Relevant cloud certifications (AWS, Azure, GCP). Experience in presenting at technology conferences or writing technical blogs. What We Offer Opportunity to work on and shape cutting-edge cloud and serverless technologies A collaborative and innovative work environment that encourages creativity and pushes technological boundaries Competitive salary and benefits package Professional development and learning opportunities Remote work options Chance to contribute to the broader tech community through open-source contributions and conference presentations

We are seeking an experienced UI/UX Designer who is passionate about creating visually stunning and clean product experiences. If you are inspired by the simplicity and usability of products like Braintrust, LangChain, or Linear, and thrive in environments where you can turn ideas into interactive prototypes quickly, AryaXAI wants to meet you. Key Responsibilities Lead Product Design: Own end-to-end UI/UX design for AryaXAI’s cloud-based platforms and tools. Rapid Prototyping: Translate product ideas and requirements into low/high-fidelity wireframes, user flows, and clickable prototypes at a rapid pace. Multiple Mockups: Generate diverse design mockups for new features and iterate quickly based on feedback. Design Systems: Build, maintain, and evolve scalable design systems, ensuring a consistent look and feel. User Research: Collaborate with users, PMs, and engineers to deeply understand pain points and define intuitive solutions. Visual Design Excellence: Deliver clean, modern, minimal UI components and layouts that delight users and enhance product usability. Cross-functional Collaboration: Work closely with engineering and product teams to ensure smooth implementation and launch of features. Continuous Improvement: Analyze user data and product usage to iterate on and improve the product’s design. Requirements Proven Experience: 4+ years designing SaaS or cloud-based products (portfolio required), ideally for B2B or developer-focused tools. Design Portfolio: Showcase of past work demonstrating clean UI, effective UX, and rapid MVP/prototyping skills. Prototyping: Expert in Figma (preferred), Sketch, Adobe XD, or similar tools for creating prototypes and mockups. Design Systems: Experience building and maintaining component libraries or design systems. Modern Aesthetics: A strong eye for visual hierarchy, typography, spacing, and modern product design principles. Rapid Iteration: Comfortable iterating quickly and managing multiple design directions simultaneously. Communication: Excellent written and verbal communication skills. Ability to articulate design decisions clearly. Team Player: Comfortable working in fast-paced, ambiguous environments and collaborating with remote/hybrid teams. Nice-to-haves Experience designing for AI, ML, or complex technical products. Familiarity with tools like Framer, Principle, or interactive prototyping frameworks. Basic knowledge of HTML/CSS or front-end development workflows.

We are seeking an experienced UI/UX Designer who is passionate about creating visually stunning and clean product experiences. If you are inspired by the simplicity and usability of products like Braintrust, LangChain, or Linear, and thrive in environments where you can turn ideas into interactive prototypes quickly, AryaXAI wants to meet you. Key Responsibilities Lead Product Design: Own end-to-end UI/UX design for AryaXAI’s cloud-based platforms and tools. Rapid Prototyping: Translate product ideas and requirements into low/high-fidelity wireframes, user flows, and clickable prototypes at a rapid pace. Multiple Mockups: Generate diverse design mockups for new features and iterate quickly based on feedback. Design Systems: Build, maintain, and evolve scalable design systems, ensuring a consistent look and feel. User Research: Collaborate with users, PMs, and engineers to deeply understand pain points and define intuitive solutions. Visual Design Excellence: Deliver clean, modern, minimal UI components and layouts that delight users and enhance product usability. Cross-functional Collaboration: Work closely with engineering and product teams to ensure smooth implementation and launch of features. Continuous Improvement: Analyze user data and product usage to iterate on and improve the product’s design. Requirements Proven Experience: 4+ years designing SaaS or cloud-based products (portfolio required), ideally for B2B or developer-focused tools. Design Portfolio: Showcase of past work demonstrating clean UI, effective UX, and rapid MVP/prototyping skills. Prototyping: Expert in Figma (preferred), Sketch, Adobe XD, or similar tools for creating prototypes and mockups. Design Systems: Experience building and maintaining component libraries or design systems. Modern Aesthetics: A strong eye for visual hierarchy, typography, spacing, and modern product design principles. Rapid Iteration: Comfortable iterating quickly and managing multiple design directions simultaneously. Communication: Excellent written and verbal communication skills. Ability to articulate design decisions clearly. Team Player: Comfortable working in fast-paced, ambiguous environments and collaborating with remote/hybrid teams. Nice-to-haves Experience designing for AI, ML, or complex technical products. Familiarity with tools like Framer, Principle, or interactive prototyping frameworks. Basic knowledge of HTML/CSS or front-end development workflows.

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. Our mission is to create AI tools that empower researchers, engineers, and organizations—including banks, financial institutions, and large enterprises—to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At AryaXAI, every team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that our work not only advances technology but also meets the rigorous demands of regulated sectors. Role Overview As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You’ll lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities Model Evaluation & Customization: Evaluate, fine-tune, and implement appropriate AI/ML models on AryaXAI.com tailored for enterprise and regulated use cases. Consider factors such as accuracy, computational efficiency, scalability, and regulatory constraints. Architectural Assessment: Assess and recommend various model architectures, ensuring that selected solutions meet the high standards required by complex business problems in financial services and other regulated industries. Enterprise Integration: Lead the deployment of AI models into production environments, ensuring seamless integration with existing enterprise systems while upholding strict compliance and security standards. Advanced AI Techniques: Drive the development and implementation of state-of-the-art AI architectures, incorporating advanced explainability, AI safety, and alignment techniques suited for regulated applications. Specialization & Innovation: Take ownership of specialized areas within machine learning/deep learning to address specific challenges related to complex datasets, regulatory requirements, and enterprise-grade AI solutions. Collaboration & Quality Assurance: Collaborate closely with Machine Learning Engineers (MLEs) and Software Development Engineers (SDEs) to roll out features, manage quality assurance, and ensure that all deployed models meet both performance and compliance benchmarks. Documentation & Compliance: Create and maintain detailed technical and product documentation, with an emphasis on auditability and adherence to regulatory standards. Qualifications Educational & Professional Background: A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors. Regulated Industry Experience (FS, Banking or Insurance is preferred) Proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings. Technical Expertise: Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.). Diverse Data Handling: Experience working with varied data types—including textual, tabular, categorical, and image data—and the ability to develop models that handle complex, enterprise-level datasets. Deployment Proficiency: Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems. Publications & Contributions: Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded.

As a research scientist at AryaXAI, you will be part of a team that is revolutionizing the field of AI with a focus on building explainable, safe, and aligned systems at scale. Our mission is to empower researchers, engineers, and organizations to leverage AI effectively while prioritizing transparency and safety. Our team is driven by a shared passion for innovation, collaboration, and excellence. In our flat organizational structure, everyone contributes actively to our mission, valuing curiosity, initiative, and outstanding performance. In this role, you will tackle advanced challenges in ML explainability, safety, and alignment. You will have the flexibility to specialize in specific areas within ML/DL and address various problem types related to these challenges. Additionally, you will be involved in creating new techniques around ML Observability & Alignment, collaborating with MLEs and SDEs to implement features, ensuring their quality and stability, and managing technical and product documentation. To excel in this position, you should have a solid academic foundation in machine learning and deep learning concepts. Hands-on experience with deep learning frameworks like Tensorflow and PyTorch is essential. You should enjoy working on diverse DL problems that involve different types of training datasets such as textual, tabular, categorical, and images. Proficiency in deploying code in cloud/on-premise environments, strong fundamentals in MLOps, and prior experience with ML explainability methods like LRP, SHAPE, LIME, IG, CEM are required. Ideally, you should have at least 2 years of hands-on experience in Deep Learning or Machine Learning, along with practical experience in implementing techniques such as Transformer models, GANs, and other deep learning paradigms. Join us at AryaXAI where startup culture meets research innovation, driving speed and reliability in AI technologies.,