Company Description Sirius AI is a US headquartered AI Consulting services and product company with operations in India. Sirius AI focuses on Financial Services enterprises and solutions / services delivered across multiple geographies. We are an innovation-driven AI and data driven consulting services firm with a high focus on delivering business outcomes for our clients. The Sirius AI team is a mix of talent from a diverse background of Consulting, Industry and Product companies which bring together their Data, Platform, Ecosystem, Technology, Project management and AI skills to develop new solutions and deliver client engagements. Role Description Intern – Data & AI Analyst Overview: As a Data & AI Analyst Intern, you’ll get hands-on experience building innovative solutions using Generative AI and machine learning. You'll work with data from different platforms, help design AI models, and support the development of tools that make data more accessible and actionable. This internship is a great opportunity to learn, grow, and contribute to real projects alongside experienced professionals. What You'll Do: • Support Client Projects: Assist in solving real-world business problems using data, AI, and analytics. • Learn on the Go: Stay curious and proactive in learning new tools, technologies, and ways of working. • AI & ML Development: Help build and test machine learning and AI models, and learn how they’re used to solve business challenges. • Collaborate with Teams: Work with data scientists, engineers, developers, and client managers to achieve project goals. • Simplify the Complex : Learn to explain technical ideas in simple ways that anyone can understand. • Work with Databases: Practice writing SQL queries and understanding how data is stored, managed, and optimized. What We're Looking For: Essential Skills: • Programming Basics: Experience with at least one language like Python, Java, or C++. • Problem Solver: A logical thinker with a curiosity to explore and innovate. • Team Player: Good communication skills and willingness to collaborate. • Database Know-How: Familiarity with relational or non-relational databases. • Linux & Tools: Basic understanding of Linux, version control (like Git), and tools like Docker or virtual environments. • AI & Data Fundamentals: Introductory knowledge of statistics, data structures, and interest in AI areas like NLP or computer vision. Bonus if You Have: • Exposure with ML libraries (e.g., TensorFlow, PyTorch) • Knowledge of Generative AI tools like Hugging Face or LangChain • Exposure to cloud platforms (AWS, Azure, GCP) • Any academic projects, certifications, or internships in data science, analytics, or cloud computing Eligibility: Applications are open exclusively to students currently enrolled at the following • Indian Institute of Technology, Kharagpur • Indian Institute of Technology, Bombay • Indian Institute of Technology Kanpur • Indian Institute of Technology Madras • Indian Institute of Technology Hyderabad Best Regards, Anmol Khachroo Assistant Manager- Human Resources Sirius AI 2nd Floor, Augusta Point, Golf Course Road DLF Phase 5, Sector 53 Gurugram, Haryana, 122002, India Anmol.khachroo@siriusai.com www.siriusai.com Show more Show less
Job Title: Executive Assistant Location: Gurugram, Haryana Experience Required : 3–6 years Work Schedule: Aligned with U.S. time zones Shift Timing: 3:00 PM – 1:00 AM IST (Monday to Friday) Work Mode: Hybrid or Remote Employment Type : Contractual (1 year) Company Overview: Sirius AI is a US headquartered AI Consulting services and product company with operations in India. Sirius AI focuses on Financial Services enterprises and solutions / services delivered across multiple geographies. We are an innovation-driven AI and data driven consulting services firm with a high focus on delivering business outcomes for our clients. The Sirius AI team is a mix of talent from a diverse background of Consulting, Industry and Product companies which bring together their Data, Platform, Ecosystem, Technology, Project management and AI skills to develop new solutions and deliver client engagements. Position Overview This position will serve as the Executive Assistant to this Senior Executive and also serve the leadership team of Sirius AI. We are seeking a highly organized and proactive Executive Assistant to provide comprehensive administrative support to our senior executives. The ideal candidate will have a proven track record of managing executive schedules, coordinating meetings, and handling confidential information with discretion. This role requires excellent communication skills, attention to detail, and the ability to multitask in a fast-paced environment. Key Responsibilities Administrative Support: Manage and maintain executives' calendars, including scheduling appointments, meetings, and travel arrangements. Coordinate and organize meetings, ensuring all necessary materials are prepared in advance. Handle rescheduling and cancellations promptly to avoid conflicts. Communication Handling: Act as the primary point of contact between executives and internal/external stakeholders. Draft, review, and send communications on behalf of executives. Screen and direct phone calls and emails to appropriate parties. Information Management: Organize and maintain confidential documents and data. Conduct research and compile information for decision-making. Task Prioritization: Assist in prioritizing tasks and tracking deadlines. Ensure efficient use of time and resources. Meeting Coordination: Coordinate meetings, prepare materials, and document minutes. Follow up on action items for timely completion. Qualifications Bachelor's degree with a strong focus in communication. 3–6 years of experience in an executive assistant or similar administrative role. Minimum of 6 years as an Executive Assistant, with at least 3 years supporting C-level executives in a global MNC or tech/software company. Willingness to work hours that align with U.S. time zones, including occasional early mornings or late evenings. Proficiency in Microsoft Office Suite (Word, Excel, PowerPoint, Outlook) and familiarity with scheduling and communication tools. Excellent organizational and time-management skills. Strong verbal and written communication abilities. Ability to maintain confidentiality and handle sensitive information with discretion. Professional demeanor and strong interpersonal skills. Show more Show less
Role Summary We are seeking a driven and technically adept Pre-Sales Consultant with 1-3 years of experience to join our growing team. This role is ideal for a professional who is passionate about AI and eager to bridge the gap between complex technical solutions and client business needs. You will play a crucial role in shaping compelling solutions, crafting persuasive proposals, and effectively communicating the value of SiriusAI's offerings to prospective clients in the financial services sector. You will leverage your ability to build powerful narratives and demonstrate tangible business value through data-driven insights. Key Responsibilities Solution Development & Value Articulation: Collaborate with technical teams to understand client challenges and design innovative AI/ML-driven solutions that align with their strategic objectives. Utilize data analytics to quantify the potential business value and ROI of proposed solutions, translating technical capabilities into tangible client benefits. Proposal & Narrative Creation: Develop high-quality, tailored proposals, presentations, and responses to RFPs and RFIs. Craft compelling business narratives around SiriusAI's solutions, demonstrating how they address client pain points and drive desired outcomes, clearly articulating the value proposition. Client Engagement & Storytelling: Engage directly with prospective clients to understand their business requirements, present solutions effectively, and build strong, trusting relationships. Employ powerful business storytelling techniques to illustrate the impact of AI solutions, making complex concepts relatable and impactful. Sales Collaboration: Work closely with the sales team to develop customized pitches, demonstrations, and sales collateral that resonate with client needs and drive business opportunities, ensuring a consistent and compelling message. Market Insight: Stay informed about industry trends, competitor offerings, and emerging technologies in AI/ML and the financial services sector to identify new business opportunities and refine solution strategies, contributing to the firm's overall market narrative. Content Creation: Contribute to the development of compelling case studies, sales collateral, and other marketing materials that highlight SiriusAI's capabilities and success stories, focusing on outcomes and value achieved for clients. Technical Understanding: Maintain a foundational understanding of AI/ML concepts, data engineering, and platform development, and how these can be applied to solve real-world business problems within financial services. Essential Skills Communication & Storytelling: Exceptional written and verbal communication skills with the ability to articulate complex technical concepts clearly and concisely to both technical and non-technical audiences. Proven ability to build compelling business narratives and use storytelling to convey value and impact. Problem-Solving & Analytical Thinking: Strong analytical and problem-solving abilities to understand client needs and translate them into effective AI-driven solutions, with an aptitude for using data to quantify business value. Collaboration: Ability to work effectively within cross-functional teams, including sales, technical delivery, and product development. Client Focus: A strong client-centric approach with the ability to build rapport and effectively address client concerns and expectations, aligning deliverables with their strategic goals. Agility: Adaptability to dynamic project scopes and priorities, with a focus on iterative improvement in a fast-paced environment. Technical Aptitude: Foundational understanding of AI and software development concepts, with a keen interest in their business applications and ability to grasp technical intricacies quickly. Qualifications Education: Bachelor's or Master’s degree in Business Administration, Management, Engineering, Computer Science, or a related field. Experience: 1-3 years of experience in a pre-sales, consulting, or solutions engineering role, ideally within the technology or AI-driven industries. Tools: High proficiency in Microsoft PowerPoint, Microsoft Excel, Tableau (or other dashboard tools) and other related tools. Familiarity with CRM software and collaboration tools. Industry Knowledge: Understanding of financial services industry trends and business processes is a plus.
Key Responsibilities Engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions that enhance decision-making and deliver tangible value. Translate business needs - particularly within financial services domains such as marketing, risk, compliance and customer lifecycle management into well-defined machine learning problem statements and solution workflows. Solve business problems using analytics and machine learning techniques: Conduct exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes. Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch. Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP. Be a master storyteller for our services and solutions to our clients at various stages of engagement such as pre-sales, sales, and delivery using data-driven insights. Stay current with developments in AI, ML modelling, and data engineering best practices, and integrate them into project work. Mentor junior team members, provide guidance on modelling practices, and contribute to an environment of continuous learning and improvement. Job Requirements 4 to 7 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment. Strong exposure to banking, payments, fintech or Wealth/Asset management domains, with experience working on problems related to: Marketing analytics for product cross-sell/up-sell and campaign optimization Customer churn and retention analysis Credit risk assessment and scoring models Fraud detection and transaction risk modeling Customer segmentation for personalized targeting Experience in developing traditional ML models across business functions such as risk, marketing, customer segmentation, and forecasting. Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch. Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring. Eagerness to learn and familiarity with developments in Agentic AI space Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting Effective communication and presentation skills for internal and client-facing interactions Ability to bridge technical solutions with business impact and drive value through data science initiatives
Key Responsibilities Support in translating business needs - particularly within financial services domains such as marketing, risk, compliance and customer lifecycle management into well-defined machine learning problem statements and solution workflows. Solve business problems using analytics and machine learning techniques: Conduct exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes. Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch. Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP. Be a master storyteller for your projects and deliverable using data-driven insights. Stay current with developments in AI, ML modelling, and data engineering best practices, and integrate them into project work. Mentor junior team members, provide guidance on modelling practices, and contribute to an environment of continuous learning and improvement. Job Requirements 2 to 5 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment. Strong exposure to banking, payments, fintech or Wealth management domains, with experience working on problems related to: Marketing analytics for product cross-sell/up-sell and campaign optimization Customer churn and retention analysis Credit risk assessment and scoring models Fraud detection and transaction risk modeling Customer segmentation for personalized targeting Experience in developing traditional ML models across business functions such as risk, marketing, customer segmentation, and forecasting. Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch. Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring. Eagerness to learn and familiarity with developments in Agentic AI space Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting Effective communication and presentation skills for internal and client-facing interactions
Key Responsibilities: Data Architecture & Management: Design and implement scalable, cloud-agnostic Data Lake, Data LakeHouse, Data Mesh and Data Fabric architectures to efficiently store, process, and manage structured and unstructured data from various sources. Data Pipeline Development : Design, develop, and maintain robust data pipelines to ingest, process, and transform data from multiple sources into usable formats for analytics and reporting using services like AWS Glue, Azure Data Factory, GCP Dataflow, Apache Spark, or Apache Airflow. Data Integration and ETL: Develop and optimize Extract, Transform, Load (ETL) and ELT processes to integrate disparate data sources into the data lake, ensuring high data quality, consistency, and reliability across multiple cloud platforms. Cloud-Agnostic Data Engineering: Develop data solutions that are cloud-agnostic, leveraging open-source technologies like Apache Spark, Delta Lake, Presto, and Kubernetes, ensuring compatibility across AWS, Azure, and GCP. Big Data Processing & Analytics: Utilize big data technologies such as Apache Spark, Hive, and Presto for distributed computing, enabling large-scale data transformations and analytics. Data Governance and Security : Implement robust data governance policies, security frameworks, and compliance controls, including role-based access control (RBAC), encryption, and monitoring to meet industry standards (GDPR, HIPAA, PCI-DSS). DevOps Integration for Data Platforms: Leverage cloud-agnostic DevOps tools and practices for source control, build automation, release management, and Infrastructure as Code (IaC) to streamline the development, deployment, and management of data lake and data architecture solutions across multiple cloud providers. Solutions should support CI/CD pipelines, automated testing, and scalable data workflows. Continuous Integration and Deployment (CI/CD ): Establish automated CI/CD pipelines to streamline deployment, testing, and monitoring of data infrastructure and workflows. Performance Optimization : Optimize data workflows and query performance using indexing, caching, and partitioning strategies to improve efficiency and cost-effectiveness. Monitoring and Troubleshooting : Implement observability solutions using tools like Prometheus, Grafana, or cloud-native monitoring services to proactively detect and resolve data pipeline issues. Collaboration and Documentation: Work with cross-functional teams, including data scientists, analysts, and business stakeholders, to design and implement scalable data solutions. Maintain comprehensive documentation of data architectures, processes, and best practices. Job Qualifications: Bachelor's degree in Computer Science, Engineering, or a related field. 6+ years of experience as a Data Engineer, specializing in cloud-agnostic data solutions and data lake architectures. Strong expertise in cloud data platforms such as AWS, Azure, and Google Cloud, with hands-on experience in services like AWS S3, Azure Data Lake, Google Cloud Storage, and related data processing tools. Proficiency in big data technologies such as Apache Spark, Hadoop, Kafka, Delta Lake, or Presto. Experience with SQL and NoSQL databases, including PostgreSQL, MySQL, and DynamoDB. Expertise in containerization and orchestration platforms such as Docker and Kubernetes. Experience implementing DevOps and CI/CD practices using Terraform, CloudFormation, or other Infrastructure as Code (IaC) tools. Knowledge of data visualization tools such as Power BI, Tableau, or Looker for presenting insights and reports. Strong problem-solving and troubleshooting skills with a proactive approach to identifying and resolving issues. Experience leading teams of 5+ cloud engineers. Preferred certifications in AWS, Azure, or Google Cloud data engineering.
Key Responsibilities : DevOps Strategy & Leadership Define and drive the overall DevOpsvision, roadmap, and best practicesacross multiple AI and cloud-native projects. Leada team of DevOps engineers, providing technical direction, mentoring, and performance management. Collaborate closely with Engineering, Security, Data, and Productteams to align DevOps initiatives with business goals. Platform Architecture & Automation Design scalable, fault-tolerant DevOpsinfrastructure across multi-cloud environments (AWS, Azure). Architect CI/CD pipelines at scale for microservices, data workflows, and AI modelsusing Jenkins, GitHub Actions, Azure DevOps, or equivalent. Institutionalize Infrastructure-as-Code (IaC) and immutable deployments via Terraform, Ansible, ARM templates, or CloudFormation. Containerization, Orchestration &Platform Engineering Design and implement Kubernetes-based container orchestration, Helm-based deployments, and EKS/AKS infrastructure. DriveGitOps and platform-as-product models for reusableinfrastructure patterns. DevSecOps & Compliance EmbedDevSecOps practices into the SDLC—security scanning, secrets management, SBOM (Software Bill of Materials), policy-as-code, and compliance automation (e.g., for PCI-DSS, SOC2). Partner with security and compliance teamsto enable continuous assurance and auditability. Monitoring, Observability & Reliability Leadenterprise-wide observability using Prometheus, Grafana,ELK, CloudWatch, Azure Monitor, and Datadog. BuildSLOs, SLIs, and incident responseprocesses for high-availability environments. Release, Change & Environment Management Standardize release governance, changemanagement workflows, and blue/green or canary deployments. Manage environments across dev, staging, and production, ensuringconsistency, quality, and security. Stakeholder & Vendor Engagement Interface with internalbusiness stakeholders and client infrastructure teams (e.g., Fiserv)for secure integration and environment access. Evaluate and manage third-party DevOps tooling and services to optimize delivery. Job Qualifications : Education & Experience Bachelor's or Master's in Computer Science,Engineering, or relateddiscipline. 7+years of hands-onDevOps experience, with 2+ years in a leadership/managerial capacity. Proven track record of managing infrastructure for cloud-native applications, especially in regulated domains like fintech or BFSI. Core Skills Deepexpertise in CI/CD tooling (Jenkins, GitHub Actions, Azure DevOps, AWS Code Pipeline). Hands-on experience with Terraform, Helm, Docker, Kubernetes, and IaC frameworks. Cloud experience acrossAWS and Azure,including IAM, VPC, ECS/EKS, S3, EC2, Lambda,and related services. Strong scripting skills (Python,Bash, PowerShell). Monitoring & Security Proficiency in observability stacks(Prometheus/Grafana, ELK/EFK, Azure Monitor). Knowledge of DevSecOps principles, vulnerability scanning,and security tooling(SonarQube, AWS Inspector, Microsoft Defender). Leadership & Collaboration Experience building and managinghigh-performing DevOps teams. Strong understanding of Agile/Scrum and software deliverylifecycle management. Ability to communicate complexideas clearly to cross-functional teams. Certifications (Preferred) AWSDevOps Engineer – Professional AzureDevOps Engineer Expert Kubernetes Administrator (CKA/CKAD) HashiCorp Certified:Terraform Associate Collaboration and Problem-Solving Skills Strong communication and teamwork abilitiesto work effectively in cross-functional teams. Excellent analytical and troubleshooting skillsto resolve technicalchallenges quickly. Benefits Work with a very innovative and collaborative firm focused on harnessing the power of AI and client’s data for cutting edge solutions. Founders are stalwarts of the AI and data consulting firms Competitive salary and performance-based bonuses Comprehensive health and wellness benefits Workon cloud technologies and continue to invest in your professional growth Collaborative and inclusive work environment
Key Responsibilities: Design, develop, and manage scalable, secure, and efficient data pipelines on AWS to process and transform large datasets from multiple sources. Implement and optimize AWS data services such as Amazon Redshift, S3, RDS, Glue, Athena, EMR, and Lambda. Architect and maintain ETL/ELT processes to ensure efficient data ingestion, transformation, and storage. Architect and implement data models on AWS platforms to support efficient data storage and retrieval. Collaborate with data architects to design and implement data lakes, data warehouses, and data marts. Build and maintain data integration workflows using tools like AWS Glue, Apache Airflow, or Step Functions. Utilize AWS DevOps for source control, build automation, release management, and infrastructure as code (IaC) to streamline the development, deployment, and management of data solutions. Implement CI/CD pipelines within AWS DevOps to automate the build, test, and deployment of data pipelines and infrastructure changes, ensuring rapid and reliable delivery of data solutions. Utilize big data technologies and frameworks such as EMR, Apache Spark, etc. to handle large volumes of data and perform complex analytics tasks. Implement real-time data processing solutions using streaming technologies like AWS Kinesis to enable timely insights and actions. Implement data governance policies and security controls within AWS environments to protect sensitive data and ensure compliance with regulatory requirements such as GDPR, HIPAA, or PCI DSS. Optimize data pipelines and processing workflows for performance, scalability, and cost-effectiveness on AWS, leveraging cloud-native services and optimizations. Monitor, troubleshoot, and optimize the performance of data pipelines and cloud-based systems to ensure high availability, low latency, and scalability. Work closely with cross-functional teams to understand business requirements and translate them into technical data solutions. Ensure data security and compliance with industry best practices and regulatory requirements (e.g., encryption, IAM roles, VPC). Conduct regular code reviews, provide feedback, and ensure adherence to best practices and standards. Keep up to date with the latest AWS services and cloud technologies to drive innovation within the team. Job Qualifications: Bachelor’s degree in computer science engineering, or a related field. 4-6 years of experience as a Cloud Data Engineer or similar role, with a strong focus on AWS cloud services. Proficiency in designing and building scalable data architectures using AWS data services such as S3, Redshift, Glue, RDS, Lambda, and EMR. Strong experience with ETL/ELT frameworks and tools like Glue, Apache Airflow, or Step Functions. Expertise in SQL, Python, or other programming languages used in data engineering. Hands-on experience with data lakes, data warehouses, and data pipeline orchestration on AWS. Familiarity with CI/CD pipelines and infrastructure-as-code (IaC) tools like CloudFormation or Terraform. Understanding of data governance, security, and compliance standards, including encryption and IAM. Experience in data modeling, data normalization, and performance optimization for cloud-based data solutions. AWS Certifications such as AWS Certified Data Analytics, AWS Certified Solutions Architect, or AWS Certified Developer. Experience with Apache Spark or Databricks on AWS. Knowledge of machine learning workflows and working with data science teams is good to have. Familiarity with DevOps practices and tools such as Docker and Kubernetes. Excellent communication and collaboration skills, with the ability to work effectively in a team environment. Strong problem-solving and troubleshooting skills, with a proactive approach to identifying and resolving issues. Ability to adapt to a fast-paced, agile environment and manage multiple priorities effectively.
Role Overview We are seeking a dynamic and visionary Associate Director to lead solutioning and innovation initiatives within our AI Innovations Lab. This role involves designing, delivering, and scaling AI/ML solutions for clients in the financial services ecosystem. The ideal candidate brings a mix of hands-on technical expertise, strategic thinking, and leadership maturity to steer high-impact engagements from concept to delivery. You will collaborate across teams to bring new ideas to life, drive innovation from the ground up, and ensure successful execution through tight alignment with client stakeholders and internal leadership. This is a highly visible, client-facing role with the opportunity to shape AI strategy for some of the most innovative players in financial services. Key Responsibilities Lead the design and delivery of AI/ML solutions across financial domains such as banking, payments, digital lending, and credit lifecycle management. Front-end with clients to translate complex business challenges into scalable AI solutions, ensuring alignment with strategic goals and measurable ROI. Manage cross-functional delivery teams, ensuring execution excellence and adherence to timelines, quality standards, and client expectations. Act as a thought leader and solution architect, continuously identifying opportunities for innovation using emerging technologies and AI algorithms. Take ownership of end-to-end AI program delivery, from ideation, POC development, and pilot testing to full-scale production deployment. Drive collaborative innovation—partnering with internal and external stakeholders to develop differentiated AI solutions tailored to financial services use cases. Provide technical mentorship and strategic direction to a high-performing team of data science consultants and AI engineers. Foster a culture of continuous experimentation and intellectual curiosity, with a focus on building from scratch and scaling impactful solutions. Required Qualifications 7 to 12 years of experience in AI, ML, or analytics, with demonstrated impact in financial services domains. Proven experience delivering AI solutions across the credit, payments, banking, or fintech landscape, preferably working with or consulting for credit providers or digital lending platforms. Deep understanding of the AI project lifecycle—from business problem formulation to deployment and post-production monitoring. Hands-on expertise with cloud platforms (AWS, Azure, GCP) and programming tools such as Python, Spark, and SQL. Strong track record of leading technical teams and interfacing with senior client stakeholders, including product heads, data leaders, and transformation executives. Bachelor’s or Master’s degree in a relevant field from a Tier 1 academic institution (Computer Science, Statistics, Mathematics, or equivalent). Preferred (Good to Have) Exposure to Generative AI, LLMs, or agent-based AI frameworks. Experience in setting up or contributing to AI-based innovations, internal solution accelerators, or Centers of Excellence (CoE).
Key Responsibilities DevOps Strategy & Leadership Define and drive the overall DevOpsvision, roadmap, and best practicesacross multiple AI and cloud-native projects. Leada team of DevOps engineers, providing technical direction, mentoring, and performance management. Collaborate closely with Engineering, Security, Data, and Productteams to align DevOps initiatives with business goals. Platform Architecture & Automation Design scalable, fault-tolerant DevOpsinfrastructure across multi-cloud environments (AWS, Azure). Architect CI/CD pipelines at scale for microservices, data workflows, and AI modelsusing Jenkins, GitHub Actions, Azure DevOps, or equivalent. Institutionalize Infrastructure-as-Code (IaC) and immutable deployments via Terraform, Ansible, ARM templates, or CloudFormation. Containerization, Orchestration &Platform Engineering Design and implement Kubernetes-based container orchestration, Helm-based deployments, and EKS/AKS infrastructure. DriveGitOps and platform-as-product models for reusableinfrastructure patterns. DevSecOps & Compliance EmbedDevSecOps practices into the SDLC—security scanning, secrets management, SBOM (Software Bill of Materials), policy-as-code, and compliance automation (e.g., for PCI-DSS, SOC2). Partner with security and compliance teamsto enable continuous assurance and auditability. Monitoring, Observability & Reliability Leadenterprise-wide observability using Prometheus, Grafana,ELK, CloudWatch, Azure Monitor, and Datadog. BuildSLOs, SLIs, and incident responseprocesses for high-availability environments. Release, Change & Environment Management Standardize release governance, changemanagement workflows, and blue/green or canary deployments. Manage environments across dev, staging, and production, ensuringconsistency, quality, and security. Stakeholder & Vendor Engagement Interface with internalbusiness stakeholders and client infrastructure teams (e.g., Fiserv)for secure integration and environment access. Evaluate and manage third-party DevOps tooling and services to optimize delivery. Job Qualifications Education & Experience Bachelor's or Master's in Computer Science,Engineering, or relateddiscipline. 7+years of hands-onDevOps experience, with 2+ years in a leadership/managerial capacity. Proven track record of managing infrastructure for cloud-native applications, especially in regulated domains like fintech or BFSI. Core Skills Deepexpertise in CI/CD tooling (Jenkins, GitHub Actions, Azure DevOps, AWS Code Pipeline). Hands-on experience with Terraform, Helm, Docker, Kubernetes, and IaC frameworks. Cloud experience acrossAWS and Azure,including IAM, VPC, ECS/EKS, S3, EC2, Lambda,and related services. Strong scripting skills (Python,Bash, PowerShell). Monitoring & Security Proficiency in observability stacks(Prometheus/Grafana, ELK/EFK, Azure Monitor). Knowledge of DevSecOps principles, vulnerability scanning,and security tooling(SonarQube, AWS Inspector, Microsoft Defender). Leadership & Collaboration Experience building and managinghigh-performing DevOps teams. Strong understanding of Agile/Scrum and software deliverylifecycle management. Ability to communicate complexideas clearly to cross-functional teams. Certifications (Preferred) AWSDevOps Engineer – Professional AzureDevOps Engineer Expert Kubernetes Administrator (CKA/CKAD) HashiCorp Certified:Terraform Associate Collaboration and Problem-Solving Skills Strong communication and teamwork abilitiesto work effectively in cross-functional teams. Excellent analytical and troubleshooting skillsto resolve technicalchallenges quickly. Benefits Work with a very innovative and collaborative firm focused on harnessing the power of AI and client’s data for cutting edge solutions. Founders are stalwarts of the AI and data consulting firms Competitive salary and performance-based bonuses Comprehensive health and wellness benefits Workon cloud technologies and continue to invest in your professional growth Collaborative and inclusive work environment
As a Senior AI and Analytics Consultant at SiriusAI, you will be responsible for driving analytics and AI initiatives in Banking analytics and Capital markets. Your primary focus will be on developing use cases that contribute to revenue generation, cost savings, and risk management for clients. You will engage with clients to understand their specific needs and leverage your expertise in building ML models in various areas such as cross-sell/up-sell, customer lifecycle/LTV, EWS, Fraud prevention, and more. Utilizing AI ops practices, containerization, and cloud-based technologies, you will develop and deploy machine learning models at scale. This will enable efficient queue management and auto-scaling capabilities to meet the demands of the U.S. financial market. You will work closely with clients to define project scope, gather requirements, and design tailored AI solutions to address specific challenges. Overseeing the entire project development lifecycle, from data collection to model building and deployment, you will ensure quality and compliance with industry standards. Additionally, you will manage and mentor a team of junior consultants and data scientists, fostering a culture of learning, collaboration, and professional growth. To excel in this role, you should have a minimum of 4 years of experience in the financial services industry, with a strong focus on banking and consulting in the U.S. market. Your expertise should include knowledge of banking industry and captives analytics, with additional exposure to areas such as Financial Risk Management, Compliance Management, Capital Markets, Wealth Management, or Asset Management. You must have proven experience in developing, deploying, and managing machine learning models using AI ops practices, containerization, and cloud platforms. Proficiency in data science tools and programming languages such as Python, SQL, or similar is essential. Strong leadership, organizational, and communication skills are also crucial as you will be leading teams and managing complex projects. Keeping abreast of the latest advancements, technologies, and regulatory changes in the U.S. financial markets is vital to continuously enhancing SiriusAI's service offerings. You should be eager to learn and implement new technologies like Generative AI in solutions and develop comprehensive proposals and presentations for client engagements. If you are passionate about leveraging AI and analytics to drive innovation in the financial sector and have a strong background in developing ML models, then this role is perfect for you. Join us at SiriusAI and be part of a dynamic team dedicated to delivering cutting-edge solutions to our clients.,
Role Overview We are seeking a dynamic and visionary Associate Director to lead solutioning and innovation initiatives within our AI Innovations Lab. This role involves designing, delivering, and scaling AI/ML solutions for clients in the financial services ecosystem. The ideal candidate brings a mix of hands-on technical expertise, strategic thinking, and leadership maturity to steer high-impact engagements from concept to delivery. You will collaborate across teams to bring new ideas to life, drive innovation from the ground up, and ensure successful execution through tight alignment with client stakeholders and internal leadership. This is a highly visible, client-facing role with the opportunity to shape AI strategy for some of the most innovative players in financial services. Key Responsibilities Lead the design and delivery of AI/ML solutions across financial domains such as banking, payments, digital lending, and credit lifecycle management. Front-end with clients to translate complex business challenges into scalable AI solutions, ensuring alignment with strategic goals and measurable ROI. Manage cross-functional delivery teams, ensuring execution excellence and adherence to timelines, quality standards, and client expectations. Act as a thought leader and solution architect, continuously identifying opportunities for innovation using emerging technologies and AI algorithms. Take ownership of end-to-end AI program delivery, from ideation, POC development, and pilot testing to full-scale production deployment. Drive collaborative innovation—partnering with internal and external stakeholders to develop differentiated AI solutions tailored to financial services use cases. Provide technical mentorship and strategic direction to a high-performing team of data science consultants and AI engineers. Foster a culture of continuous experimentation and intellectual curiosity, with a focus on building from scratch and scaling impactful solutions. Required Qualifications 7 to 12 years of experience in AI, ML, or analytics, with demonstrated impact in financial services domains. Proven experience delivering AI solutions across the credit, payments, banking, or fintech landscape, preferably working with or consulting for credit providers or digital lending platforms. Deep understanding of the AI project lifecycle—from business problem formulation to deployment and post-production monitoring. Hands-on expertise with cloud platforms (AWS, Azure, GCP) and programming tools such as Python, Spark, and SQL. Strong track record of leading technical teams and interfacing with senior client stakeholders, including product heads, data leaders, and transformation executives. Bachelor’s or Master’s degree in a relevant field from a Tier 1 academic institution (Computer Science, Statistics, Mathematics, or equivalent). Preferred (Good to Have) Exposure to Generative AI, LLMs, or agent-based AI frameworks. Experience in setting up or contributing to AI-based innovations, internal solution accelerators, or Centers of Excellence (CoE).
Key Responsibilities Engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions that enhance decision-making and deliver tangible value. Translate business needs - particularly within financial services domains such as marketing, risk, compliance and customer lifecycle management into well-defined machine learning problem statements and solution workflows. Solve business problems using analytics and machine learning techniques: Conduct exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes. Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch. Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP. Be a master storyteller for our services and solutions to our clients at various stages of engagement such as pre-sales, sales, and delivery using data-driven insights. Stay current with developments in AI, ML modelling, and data engineering best practices, and integrate them into project work. Mentor junior team members, provide guidance on modelling practices, and contribute to an environment of continuous learning and improvement. Job Requirements 4 to 7 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment. Strong exposure to banking, payments, fintech or Wealth/Asset management domains, with experience working on problems related to: Marketing analytics for product cross-sell/up-sell and campaign optimization Customer churn and retention analysis Credit risk assessment and scoring models Fraud detection and transaction risk modeling Customer segmentation for personalized targeting Experience in developing traditional ML models across business functions such as risk, marketing, customer segmentation, and forecasting. Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch. Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring. Eagerness to learn and familiarity with developments in Agentic AI space Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting Effective communication and presentation skills for internal and client-facing interactions Ability to bridge technical solutions with business impact and drive value through data science initiatives
Role Overview We are seeking a dynamic and visionary Associate Director to lead solutioning and innovation initiatives within our AI Innovations Lab. This role involves designing, delivering, and scaling AI/ML solutions for clients in the financial services ecosystem. The ideal candidate brings a mix of hands-on technical expertise, strategic thinking, and leadership maturity to steer high-impact engagements from concept to delivery. You will collaborate across teams to bring new ideas to life, drive innovation from the ground up, and ensure successful execution through tight alignment with client stakeholders and internal leadership. This is a highly visible, client-facing role with the opportunity to shape AI strategy for some of the most innovative players in financial services. Key Responsibilities Lead the design and delivery of AI/ML solutions across financial domains such as banking, payments, digital lending, and credit lifecycle management. Front-end with clients to translate complex business challenges into scalable AI solutions, ensuring alignment with strategic goals and measurable ROI. Manage cross-functional delivery teams, ensuring execution excellence and adherence to timelines, quality standards, and client expectations. Act as a thought leader and solution architect, continuously identifying opportunities for innovation using emerging technologies and AI algorithms. Take ownership of end-to-end AI program delivery, from ideation, POC development, and pilot testing to full-scale production deployment. Drive collaborative innovationpartnering with internal and external stakeholders to develop differentiated AI solutions tailored to financial services use cases. Provide technical mentorship and strategic direction to a high-performing team of data science consultants and AI engineers. Foster a culture of continuous experimentation and intellectual curiosity, with a focus on building from scratch and scaling impactful solutions. Required Qualifications 7 to 12 years of experience in AI, ML, or analytics, with demonstrated impact in financial services domains. Proven experience delivering AI solutions across the credit, payments, banking, or fintech landscape, preferably working with or consulting for credit providers or digital lending platforms. Deep understanding of the AI project lifecyclefrom business problem formulation to deployment and post-production monitoring. Hands-on expertise with cloud platforms (AWS, Azure, GCP) and programming tools such as Python, Spark, and SQL. Strong track record of leading technical teams and interfacing with senior client stakeholders, including product heads, data leaders, and transformation executives. Bachelors or Masters degree in a relevant field from a Tier 1 academic institution (Computer Science, Statistics, Mathematics, or equivalent). Preferred (Good to Have) Exposure to Generative AI, LLMs, or agent-based AI frameworks. Experience in setting up or contributing to AI-based innovations, internal solution accelerators, or Centers of Excellence (CoE). Show more Show less
As an Associate Director in the AI Innovations Lab, your role involves designing, delivering, and scaling AI/ML solutions for clients in the financial services ecosystem. You will collaborate across teams to drive innovation, ensuring successful execution through alignment with client stakeholders and internal leadership. This role offers the opportunity to shape AI strategy for innovative players in financial services. Key Responsibilities: - Front-end with clients to translate complex business challenges into scalable AI solutions aligned with strategic goals and measurable ROI. - Manage cross-functional delivery teams to ensure execution excellence, adherence to timelines, quality standards, and client expectations. - Act as a thought leader and solution architect, identifying opportunities for innovation using emerging technologies and AI algorithms. - Take ownership of end-to-end AI program delivery, from ideation to full-scale production deployment. - Drive collaborative innovation by partnering with stakeholders to develop AI solutions tailored to financial services use cases. - Provide technical mentorship and strategic direction to a team of data science consultants and AI engineers. - Foster a culture of continuous experimentation and intellectual curiosity focused on building and scaling impactful solutions. Required Qualifications: - 7 to 12 years of experience in AI, ML, or analytics, with demonstrated impact in financial services domains. - Deep understanding of the AI project lifecycle, from problem formulation to deployment and post-production monitoring. - Hands-on expertise with cloud platforms (AWS, Azure, GCP) and programming tools like Python, Spark, and SQL. - Strong track record of leading technical teams and interfacing with senior client stakeholders. - Bachelors or Masters degree in Computer Science, Statistics, Mathematics, or equivalent from a Tier 1 academic institution. Preferred (Good to Have): - Exposure to Generative AI, LLMs, or agent-based AI frameworks. - Experience in setting up or contributing to AI-based innovations or Centers of Excellence (CoE).,
Role Overview: You will engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions that enhance decision-making and deliver tangible value. You will translate business needs, especially within financial services domains such as marketing, risk, compliance, and customer lifecycle management, into well-defined machine learning problem statements and solution workflows. Your role will involve solving business problems using analytics and machine learning techniques, conducting exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes. Additionally, you will develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. Designing and implementing scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch will be part of your responsibilities. Collaboration with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP is essential. You will also be a master storyteller for services and solutions to clients at various engagement stages such as pre-sales, sales, and delivery using data-driven insights. Staying current with developments in AI, ML modeling, and data engineering best practices, and integrating them into project work will be key. Mentoring junior team members, providing guidance on modeling practices, and contributing to an environment of continuous learning and improvement are also part of your role. Key Responsibilities: - Engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions. - Translate business needs within financial services domains into well-defined machine learning problem statements and solution workflows. - Solve business problems using analytics and machine learning techniques, conducting exploratory data analysis, feature engineering, and model development. - Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization. - Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch. - Collaborate with cross-functional teams to deliver robust solutions in cloud environments. - Be a master storyteller for services and solutions to clients at various engagement stages using data-driven insights. - Stay current with developments in AI, ML modeling, and data engineering best practices. - Mentor junior team members and contribute to an environment of continuous learning and improvement. Qualification Required: - 4 to 7 years of relevant experience in building ML solutions, with a strong foundation in machine learning modeling and deployment. - Experience in marketing analytics, customer churn analysis, credit risk assessment, fraud detection, and customer segmentation. - Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch. - Bachelors or Masters degree from a Tier 1 technical institute or MBA from Tier 1 institute. - Eagerness to learn and familiarity with developments in Agentic AI space. - Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting. - Effective communication and presentation skills for internal and client-facing interactions. - Ability to bridge technical solutions with business impact and drive value through data science initiatives.,