Job Responsibilities Design, develop, and deploy machine learning based models for credit risk and related use cases. Present model outcomes and recommendations to internal and external stakeholders. Establish a regular cadence of client engagement including roadmap planning, updates, and reporting. Establish model monitoring, back-testing, and performance tracking frameworks. Work closely with internal teams including Client Engagement, Engineering and Product to ensure smooth integration and : 4+ years of experience in data science and machine learning with prior experience in credit risk model development. Strong proficiency in Python for data analysis and model development. Sound knowledge of supervised and unsupervised machine learning techniques. Strong communication and presentation skills - ability to explain complex models to stakeholders. Experience managing client relationships or working in a consulting environment Bachelor/masters degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science, Mathematics). (ref:hirist.tech)
Job Description Experienced Java developer with over 2-3 years of hands-on experience in designing, developing, and maintaining Java-based applications. Skilled in full-stack development, with expertise in Java EE, Spring Framework, Hibernate, front-end technologies and strong knowledge in RDBMS. Adept at collaborating with cross-functional teams to deliver high-quality software solutions that meet business requirements and exceed expectations. Technical Skills Programming Languages : Java, JavaScript, HTML, CSS Frameworks and Libraries : Spring Framework (Spring Boot, Spring MVC, Spring Data), Hibernate, AngularJS, ReactJS Database Systems : MySQL, PostgreSQL, MongoDB Tools and Technologies : Maven, Gradle, Git, Jenkins, Docker, Kubernetes, AWS Development Methodologies : Agile, Scrum Scrum/Agile development management including providing regular updates during Scrum meetings. (ref:hirist.tech)
Responsibilities Design, develop, and maintain interactive dashboards and reports in Tableau to support business needs. Optimize Tableau dashboards for performance, scalability, and usability. Develop complex calculations, parameters, LOD expressions, and custom visualizations in Tableau. Work closely with business analysts, data engineers, and stakeholders to translate business requirements into meaningful visual analytics. Create and maintain data models, data blending, and relationships within Tableau. Implement governance and security protocols for Tableau Cloud /Server environments. Provide training and support to business users on self-service analytics using You Are : 8+ years of experience in Tableau development and BI reporting. Strong proficiency in SQL (writing complex queries, stored procedures, performance tuning) Experience working with databases such as SQL Server, Snowflake, Redshift, BigQuery, etc. Strong understanding of data warehousing, dimensional modeling, and ETL processes Experience with Tableau Cloud/Server administration (publishing, security, permissions). Knowledge of data visualization best practices and UX/UI Qualifications : Tableau Desktop & Server Certification (e.g., Tableau Certified Data Analyst, Tableau Desktop Specialist). (ref:hirist.tech)
The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. Responsibilities Analyze raw data: assessing quality, cleansing, structuring for downstream processing Design accurate and scalable prediction algorithms Collaborate with engineering team to bring analytical prototypes to production Generate actionable insights for business improvements Qualifications Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) At least 1 - 2 years' of experience in quantitative analytics or data modeling Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms Fluency in a programming language (Python)
Job Responsibilities: Own the design, development, and maintenance input mapper and rollup logic for credit bureau, core banking, internal datasets and any additional data sources in the future Ensure accuracy, performance, and compliance through rigorous QC frameworks and automated validation tools. Collaborate with credit risk, product, engineering, and integration teams to deploy and support model and strategy features in production environments. Analyse complex datasets and derive accurate business logic in alignment with credit policy and model requirements. Lead and mentor a team of data scientists and analysts, ensuring adherence to best practices in software engineering and data science. Contribute to data governance, documentation, and version control of the CDM and associated tooling. Requirements: 5+ years of experience in Data Science/Data Engineering/Analytics Strong Python skills – ability to writing clean, testable, modular, and production- grade code. Experience in credit risk, fintech, or financial services domains. Experience working on US credit bureau data Prior experience managing or mentoring technical teams. Strong communication skills and the ability to work cross-functionally with engineers, product managers, and stakeholders. Bachelor’s / master’s degree in a quantitative discipline (e.g. Engineering, Computer Science, Mathematics).
Responsibilities Design, develop, and maintain interactive dashboards and reports in Tableau to support business needs. Optimize Tableau dashboards for performance, scalability, and usability. Develop complex calculations, parameters, LOD expressions, and custom visualizations in Tableau. Work closely with business analysts, data engineers, and stakeholders to translate business requirements into meaningful visual analytics. Create and maintain data models, data blending, and relationships within Tableau. Implement governance and security protocols for Tableau Cloud /Server environments. Provide training and support to business users on self-service analytics using Tableau. Requirements You Are 2+ years of experience in Tableau development and BI reporting. Strong proficiency in SQL (writing complex queries, stored procedures, performance tuning) Experience working with databases such as SQL Server, Snowflake, Redshift, BigQuery, etc. Strong understanding of data warehousing, dimensional modeling, and ETL processes Experience with Tableau Cloud/Server administration (publishing, security, permissions). Knowledge of data visualization best practices and UX/UI Qualifications : Tableau Desktop & Server Certification (e.g., Tableau Certified Data Analyst, Tableau Desktop Specialist). (ref:hirist.tech)
Scienaptic is on a mission to increase credit availability by transforming technology used in credit decisioning. Over 150 years of credit experience is embedded in Scienaptic's AI native credit decision platform. Our clients across banks, credit unions, fintech, and other lenders use the platform to constantly improve the quality of underwriting decisions. This enables them to say ‘yes’ to borrowers more often and faster. The Data Scientist role at Scienaptic will enable you to be at the forefront of latest cutting- edge technology and create a significant and visible business impact for Scienaptic. You will be working with some of the best-in-class Engineers, Data Scientists, and Business Analytics Consultants in an environment which will encourage you to contribute widely to functional and technological aspects without worrying about conventional job silos. Job Responsibilities: Design, develop, and deploy credit risk models and strategies, Lead the development of decision systems for SMB lending. Work closely with client teams to define problem statements, validate solutions, and drive adoption. Present model/strategy outcomes and recommendations to internal and external stakeholders. Work closely with internal teams including Client Engagement, Engineering and Product to ensure smooth integration and deployment. Collaborate with product, engineering, and analytics teams to integrate our product into client workflows and platforms. Develop repeatable frameworks and toolkits to accelerate deployment and monitoring across clients. Requirements: 4+ years of experience in data science and machine learning with prior experience in credit risk, fintech, or financial services domains. Prior experience working in SMB lending Prior experience working in SMB lending in the US market (good to have) Experience working with cross-functional teams including product, business, and engineering. Strong client-facing and communication skills - ability to translate business goals into analytical frameworks. Hands-on experience with Python Bachelor/master’s degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science, Mathematics).
We are in the process of enhancing our platform team to perform our product implementations and workflow automations across markets. We are seeking a highly skilled Senior Platform Engineer with experience in Python/Java/Scala Application frameworks, SQL and a strong foundation in cloud technologies like Kubernetes and Docker. Knowledge of Data Engineering and integration with third-party APIs will be a plus. As a key member of the SaaS platform team, You will be responsible for defining the workflow, fit our product into that workflow, deploy our product and deliver the workflow on the product including any additional utilities and upstream/downstream system integrations required. You will play a crucial role in defining platform strategies, driving automation, improving reliability, and ensuring high availability of the system. Key Responsibilities: Design, develop, and deploy workflows : Implement end-to-end workflows and integrate them with upstream and downstream systems. Build and maintain third party API integrations : Write clean, scalable, and efficient Python code leveraging popular frameworks such as Flask , FastAPI ,Spring , Play. Cloud Infrastructure Development : Work with cloud platforms (AWS) and use Kubernetes and Docker to deploy and scale applications. Data Pipeline Development : Collaborate with the data engineering team to integrate Python applications with SQL/NoSQL databases and manage ETL pipelines. DevOps Practices : Ensure smooth CI/CD, logging, and monitoring of platform services. Develop utilities and integration components : Build reusable utilities and components required for workflow automation and integration with other services. Security enhancement : Collaborate with the security team to ensure platform security, implementing best practices in identity management, and data protection. Collaborate with cross-functional teams : Work with engineering, product, and data science and QA teams to deliver robust SaaS platform features. Qualifications: 5+ years of coding experience with Python, Java, Scala, SQL, and associated frameworks (Flask, Django, Spring , Play framework etc.). Experience with Data Engineering concepts and tools like ETL pipelines, data modeling, and SQL-based databases. Exposure to Big Data tools like Spark (scala/python), Kafka, or Airflow would be an added advantage. Proficient in working with RESTful APIs and third-party API integrations. Familiarity with CI/CD tools like Jenkins, GitLab, or similar. Excellent problem-solving skills and a passion for automation and workflow improvement. Experience in building services for SaaS platforms with containerization technologies like Docker and Kubernetes is a plus.
Senior Product Manager — AI-Powered Credit Underwriting About Scienaptic Scienaptic is the world’s leading AI-powered credit underwriting platform. Built with seasoned Chief Risk Officers, our platform delivers higher approvals (15–40%) and lower credit losses (10–25%) with full regulatory explainability. In the past year alone, we helped financial institutions evaluate 45M+ consumers and extend credit to 15M+ people. Our customers span Fortune 100 banks, community banks, and fintechs across multiple countries. The role We’re hiring a Senior Product Manager to own strategy and end-to-end delivery for Scienaptic’s digital credit underwriting products and upstream integrations with Loan Origination Systems (LOS). You’ll define the multi-quarter roadmap, drive cross-functional execution with Engineering, Data Science, and GTM, and ship products that materially improve approval rates, loss outcomes, and operational efficiency for lenders. What you’ll do Set strategy & outcomes: Define vision, north-star metrics, and a multi-quarter roadmap for AI/ML underwriting and LOS integrations; align execs and partner teams. Lead discovery: Partner with customers (underwriters, risk analysts, credit ops, compliance) to frame problems, validate hypotheses, and translate insights into clear product requirements and narratives/PRDs. Ship platform capabilities: Drive standard product features and reusable components (requirements, functional/technical specs, usability, integration patterns) that scale across consumer and SME lending. Own delivery: Run prioritization and sequencing; break strategy into epics/stories; collaborate daily with Engineering and Data Science in a scrum/kanban process to launch high-quality releases. Instrument & iterate: Define success metrics and telemetry; design experiments; analyze lift (approvals, losses, time-to-decision) and continuously refine models, policies, and workflows. Integrations & ecosystem: Establish and evolve common integration requirements with LOS and data providers; reduce time-to-implement through standards, SDKs, and documentation. Market & competitive insight: Track credit risk trends, regulatory developments, and competitor moves; translate insights into roadmap choices. Go-to-market partnership: Coordinate launches with Sales, Marketing, and Professional Services; contribute to pricing/packaging, solution playbooks, and enablement. Customer experience: Champion a user-centric UX that drives adoption and stickiness across stakeholders (underwriters, risk, analytics, and executives). Raise the bar: Mentor PMs/associates and improve product rituals, decision quality, and execution velocity. What you’ve done (requirements) 8+ years of product management (or equivalent), with 3+ years owning a complex B2B platform or data product end-to-end. Domain expertise in consumer or SME lending with exposure to digital underwriting and LOS ecosystems. Proven record shipping ML- or rules-driven decisioning products with measurable business impact (e.g., approval lift, loss reduction, Ops efficiency). Strong product discovery toolkit (user research, JTBD, problem framing, experiment design) and exceptional written communication (clear specs, narratives, and stakeholder docs). Analytical fluency—comfortable with metrics, dashboards, and basic SQL/BI; able to define instrumentation and experimentation. Technical depth to collaborate on APIs, data models, scoring policies, and system trade-offs (no need to code, but you can reason about complexity and risk). Experience leading cross-functional teams and resolving complex prioritization across executives, GTM, Delivery/PS, and Engineering. Nice to have Experience with model governance and regulatory explainability in credit (e.g., reason codes, adverse action workflows). Background in pricing/packaging and commercial strategy for enterprise SaaS. Platform/ecosystem chops (developer docs, SDKs, integration frameworks). Location & benefits Location: [Remote/Hybrid] Compensation: Competitive salary, bonus, and equity. Benefits: Comprehensive health benefits, retirement plan, and professional development support. Equal Opportunity Scienaptic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
The lead Data Scientist role at Scienaptic will enable you to be at the forefront of latest cutting-edge technology and create a significant and visible business impact for Scienaptic. You will be working with some of the best-in-class Engineers, Data Scientists, and Business Analytics Consultants in an environment which will encourage you to contribute widely to functional and technological aspects without worrying about conventional job silos. Job Responsibilities: • Design, develop, and deploy machine learning based models for credit risk and related use cases. • Present model outcomes and recommendations to internal and external stakeholders. • Establish a regular cadence of client engagement including roadmap planning, updates, and reporting. • Establish model monitoring, back-testing, and performance tracking frameworks. • Work closely with internal teams including Client Engagement, Engineering and Product to ensure smooth integration and deployment. Requirements: • 8+ years of experience in data science and machine learning with prior experience in credit risk model development. • Strong proficiency in Python for data analysis and model development. • Sound knowledge of supervised and unsupervised machine learning techniques. • Strong communication and presentation skills - ability to explain complex models to stakeholders. • Experience managing client relationships or working in a consulting environment • Bachelor/master’s degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science, Mathematics).
Company Description Scienaptic AI is dedicated to expanding access to credit by transforming the technology behind credit decision-making. With over 150 years of credit expertise built into its AI-powered platform, Scienaptic helps financial institutions like banks, credit unions, fintech companies, and other lenders improve their underwriting decisions. The platform allows users to enhance decision quality, enabling faster approvals and more opportunities to say “yes” to borrowers. Scienaptic’s mission-driven approach empowers lenders to create better financial outcomes for their clients. Role Description This is a full-time remote role for a Lead Credit Modelling professional. In this role, you will manage and develop credit models, analyze data to improve decision-making processes, and work collaboratively with finance and credit teams to optimize credit strategies. Day-to-day tasks will involve interpreting credit risk data, delivering insights to improve credit policies, and ensuring customer-focused service initiatives are supported. Communication with stakeholders and the ability to provide data-driven guidance will be integral to the role. Qualifications 1+ years' experience with Data science Strong Analytical Skills to interpret data and improve credit decision-making Good with python skills Excellent Communication abilities for stakeholder collaboration and reporting Proficiency in data analysis tools and techniques is a plus Graduate degree in Finance, Economics, Business, or a related field is preferred
Company Description Scienaptic AI is dedicated to expanding access to credit by transforming the technology behind credit decision-making. With over 150 years of credit expertise built into its AI-powered platform, Scienaptic helps financial institutions like banks, credit unions, fintech companies, and other lenders improve their underwriting decisions. The platform allows users to enhance decision quality, enabling faster approvals and more opportunities to say “yes” to borrowers. Scienaptic’s mission-driven approach empowers lenders to create better financial outcomes for their clients. Role Description This is a full-time remote role for a Lead Credit Modelling professional. In this role, you will manage and develop credit models, analyze data to improve decision-making processes, and work collaboratively with finance and credit teams to optimize credit strategies. Day-to-day tasks will involve interpreting credit risk data, delivering insights to improve credit policies, and ensuring customer-focused service initiatives are supported. Communication with stakeholders and the ability to provide data-driven guidance will be integral to the role. Qualifications Proven experience in developing credit models Strong Analytical Skills to interpret data and improve credit decision-making Background in Finance with an understanding of its application to credit strategies Exceptional Customer Service skills to ensure client-focused outcomes Excellent Communication abilities for stakeholder collaboration and reporting Proficiency in data analysis tools and techniques is a plus Graduate degree in Finance, Economics, Business, or a related field is preferred
Job Title: Lead Platform Engineer – Agentic System Location: Bengaluru Department: Engineering – AI Platform Experience: 8+ years (with a minimum of 2 years of hands-on experience in designing, architecting production grade Agentic AI and LLM-based systems with all NFRs covered.) About the Role Our organization is developing an Agentic AI Platform designed to orchestrate intelligent, autonomous workflows that drive decision-making, automation, and innovation across diverse domains. As the Lead AI Platform Engineer , you will be responsible for architecting and implementing the foundational elements of this platform. This includes designing, deploying and maintaining production grade multi-agent systems with most effective frameworks including CrewAI, LangGraph , or equivalent systems, and deploying these solutions at scale on modern cloud infrastructure. You will work on and collaborate closely with other data engineers, data scientists, ML engineers, and product teams to realize our vision of adaptive, reasoning-driven systems. Key Responsibilities Architect & Build Agentic Systems: Design, develop, and deploy multi-agent workflows leveraging frameworks such as CrewAI, LangGraph , or custom-built orchestration layers. Platform Engineering: Construct the underlying agentic runtime , encompassing message routing, memory management, and context-sharing mechanisms between agents. LLM & Tool Integration: Integrate Large Language Models (e.g., OpenAI, Anthropic, or open-source alternatives), vector databases, retrieval systems, and external APIs for agent tool-use and reasoning capabilities. Workflow Design & Optimization: Collaborate with AI researchers and solution engineers to design dynamic agent workflows that adapt based on contextual information and analytical results. Cloud & Scalability: Architect scalable, cost-efficient deployments utilizing AWS, GCP, or Azure , leveraging cloud-native components (e.g., Lambda, ECS, Kubernetes, Pub/Sub) to ensure high availability and performance. Observability & Governance: Implement comprehensive monitoring, evaluation metrics, and safety checks for autonomous agents to ensure reliability and compliance. Team Leadership & Mentorship: Provide guidance to a team of engineers, establish best practices for agent development, and cultivate a high-performance engineering culture. Take sessions for the team and build tog-grade skills in the area across the company. Required Qualifications Strong background in Python , with demonstrated experience in asynchronous programming, API development, and distributed systems. Proven experience in building LLM-powered multi-agent systems using CrewAI , LangGraph , LangChain , or similar orchestration frameworks. Deep understanding of prompt engineering , RAG pipelines , and tool-calling mechanisms . Hands-on experience with cloud infrastructure (AWS/GCP/Azure) and MLOps components (e.g., Kubernetes, Docker, CI/CD, API Gateway). Solid understanding of state management , context persistence , and memory architecture for agents. Experience integrating vector stores (e.g., FAISS, Pinecone, Chroma, Weaviate) and LLM APIs (e.g., OpenAI, Claude, Gemini). Demonstrated ability to architect scalable AI systems from prototype to production. Excellent communication and collaboration skills, with the ability to translate complex technical concepts into actionable platform features. Experience with Graph-based orchestration or LangGraph advanced workflows . Familiarity with CrewAI “crew and task” paradigms or other agent coordination frameworks. Nice-to-Have Exposure to event-driven architectures , message queues , or knowledge graphs . Understanding of AI safety, alignment , and governance principles . Contributions to open-source agent frameworks or AI orchestration tools. Ability to customize models to specific purposes. What We Offer An unparalleled opportunity to build the next-generation Agentic AI Platform from inception. The chance to work with cutting-edge LLM orchestration technologies and cloud-native AI systems . Competitive compensation and potential equity options. A collaborative, innovation-driven culture with a strong focus on real-world impact.
We’re hiring a Senior /Lead Product Manager to own strategy and end-to-end delivery for Scienaptic’s digital credit underwriting products and upstream integrations with Loan Origination Systems (LOS). You’ll define the multi-quarter roadmap, drive cross-functional execution with Engineering, Data Science, and GTM, and ship products that materially improve approval rates, loss outcomes, and operational efficiency for lenders. What you’ll do ● Set strategy & outcomes: Define vision, north-star metrics, and a multi-quarter roadmap for AI/ML underwriting and LOS integrations; align execs and partner teams. ● Lead discovery: Partner with customers (underwriters, risk analysts, credit ops, compliance) to frame problems, validate hypotheses, and translate insights into clear product requirements and narratives/PRDs. ● Ship platform capabilities: Drive standard product features and reusable components (requirements, functional/technical specs, usability, integration patterns) that scale across consumer and SME lending. ● Own delivery: Run prioritization and sequencing; break strategy into epics/stories; collaborate daily with Engineering and Data Science in a scrum/kanban process to launch high-quality releases. ● Instrument & iterate: Define success metrics and telemetry; design experiments; analyze lift (approvals, losses, time-to-decision) and continuously refine models, policies, and workflows. ● Integrations & ecosystem: Establish and evolve common integration requirements with LOS and data providers; reduce time-to-implement through standards, SDKs, and documentation. ● Market & competitive insight: Track credit risk trends, regulatory developments, and competitor moves; translate insights into roadmap choices. ● Go-to-market partnership: Coordinate launches with Sales, Marketing, and Professional Services; contribute to pricing/packaging, solution playbooks, and enablement. ● Customer experience: Champion a user-centric UX that drives adoption and stickiness across stakeholders (underwriters, risk, analytics, and executives). ● Raise the bar: Mentor PMs/associates and improve product rituals, decision quality, and execution velocity. What you’ve done (requirements) ● 8+ years of product management (or equivalent), with 3+ years owning a complex B2B platform or data product end-to-end. ● Domain expertise in consumer or SME lending with exposure to digital underwriting and LOS ecosystems. ● Proven record shipping ML- or rules-driven decisioning products with measurable business impact (e.g., approval lift, loss reduction, Ops efficiency). ● Strong product discovery toolkit (user research, JTBD, problem framing, experiment design) and exceptional written communication (clear specs, narratives, and stakeholder docs). ● Analytical fluency—comfortable with metrics, dashboards, and basic SQL/BI; able to define instrumentation and experimentation. ● Technical depth to collaborate on APIs, data models, scoring policies, and system trade- offs (no need to code, but you can reason about complexity and risk). ● Experience leading cross-functional teams and resolving complex prioritization across executives, GTM, Delivery/PS, and Engineering. Nice to have ● Experience with model governance and regulatory explainability in credit (e.g., reason codes, adverse action workflows). ● Background in pricing/packaging and commercial strategy for enterprise SaaS. ● Platform/ecosystem chops (developer docs, SDKs, integration frameworks).
Description Job Title : Lead Platform Engineer Agentic System. Location : Bengaluru. Department : Engineering AI Platform. Experience : 8+ years (with a minimum of 2 years of hands-on experience in designing, architecting production grade Agentic AI and LLM-based systems with all NFRs covered. About The Role Our organization is developing an Agentic AI Platform designed to orchestrate intelligent, autonomous workflows that drive decision-making, automation, and innovation across diverse domains. As the Lead AI Platform Engineer, you will be responsible for architecting and implementing the foundational elements of this platform. This includes designing, deploying and maintaining production grade multi-agent systems with most effective frameworks including CrewAI, LangGraph, or equivalent systems, and deploying these solutions at scale on modern cloud infrastructure. You will work on and collaborate closely with other data engineers, data scientists, ML engineers, and product teams to realize our vision of adaptive, reasoning-driven systems. Key Responsibilities Architect & Build Agentic Systems : Design, develop, and deploy multi-agent workflows leveraging frameworks such as CrewAI, LangGraph, or custom-built orchestration layers. Platform Engineering : Construct the underlying agentic runtime, encompassing message routing, memory management, and context-sharing mechanisms between agents. LLM & Tool Integration : Integrate Large Language Models (e.g., OpenAI, Anthropic, or open-source alternatives), vector databases, retrieval systems, and external APIs for agent tool-use and reasoning capabilities. Workflow Design & Optimization : Collaborate with AI researchers and solution engineers to design dynamic agent workflows that adapt based on contextual information and analytical results. Cloud & Scalability : Architect scalable, cost-efficient deployments utilizing AWS, GCP, or Azure, leveraging cloud-native components (e.g., Lambda, ECS, Kubernetes, Pub/Sub) to ensure high availability and performance. Observability & Governance : Implement comprehensive monitoring, evaluation metrics, and safety checks for autonomous agents to ensure reliability and compliance. Team Leadership & Mentorship : Provide guidance to a team of engineers, establish best practices for agent development, and cultivate a high-performance engineering culture. Take sessions for the team and build tog-grade skills in the area across the company. Required Qualifications Strong background in Python, with demonstrated experience in asynchronous programming, API development, and distributed systems. Proven experience in building LLM-powered multi-agent systems using CrewAI, LangGraph, LangChain, or similar orchestration frameworks. Deep understanding of prompt engineering, RAG pipelines, and tool-calling mechanisms. Hands-on experience with cloud infrastructure (AWS/GCP/Azure) and MLOps components (e.g., Kubernetes, Docker, CI/CD, API Gateway). Solid understanding of state management, context persistence, and memory architecture for agents. Experience integrating vector stores (e.g., FAISS, Pinecone, Chroma, Weaviate) and LLM APIs (e.g., OpenAI, Claude, Gemini). Demonstrated ability to architect scalable AI systems from prototype to production. Excellent communication and collaboration skills, with the ability to translate complex technical concepts into actionable platform features. Experience with Graph-based orchestration or LangGraph advanced workflows. Familiarity with CrewAI crew and task paradigms or other agent coordination frameworks. Nice-to-Have Exposure to event-driven architectures, message queues, or knowledge graphs. Understanding of AI safety, alignment, and governance principles. Contributions to open-source agent frameworks or AI orchestration tools. Ability to customize models to specific purposes. What We Offer An unparalleled opportunity to build the next-generation Agentic AI Platform from inception. The chance to work with cutting-edge LLM orchestration technologies and cloud-native AI systems. Competitive compensation and potential equity options. A collaborative, innovation-driven culture with a strong focus on real-world impact. (ref:hirist.tech)
Description Were hiring a Senior /Lead Product Manager to own strategy and end-to-end delivery for Scienaptics digital credit underwriting products and upstream integrations with Loan Origination Systems (LOS). Youll define the multi-quarter roadmap, drive cross-functional execution with Engineering, Data Science, and GTM, and ship products that materially improve approval rates, loss outcomes, and operational efficiency for lenders. What Youll Do Set strategy & outcomes : Define vision, north-star metrics, and a multi-quarter roadmap for AI/ML underwriting and LOS integrations; align execs and partner teams. Lead discovery : Partner with customers (underwriters, risk analysts, credit ops compliance) to frame problems, validate hypotheses, and translate insights into clear product requirements and narratives/PRDs. Ship platform capabilities : Drive standard product features and reusable components (requirements functional/technical specs, usability, integration patterns) that scale across consumer and SME lending. Own delivery : Run prioritization and sequencing; break strategy into epics/stories collaborate daily with Engineering and Data Science in a scrum/kanban process to launch high-quality releases. Instrument & iterate : Define success metrics and telemetry; design experiments; analyze lift (approvals losses, time-to-decision) and continuously refine models, policies, and workflows. Integrations & ecosystem : Establish and evolve common integration requirements with LOS and data providers; reduce time-to-implement through standards, SDKs, and documentation. Market & competitive insight : Track credit risk trends, regulatory developments, and competitor moves; translate insights into roadmap choices. Go-to-market partnership : Coordinate launches with Sales, Marketing, and Professional Services; contribute to pricing/packaging, solution playbooks, and enablement. Customer experience : Champion a user-centric UX that drives adoption and stickiness across stakeholders (underwriters, risk, analytics, and executives). Raise the bar : Mentor PMs/associates and improve product rituals, decision quality, and execution velocity. What Youve Done (requirements) 8+ years of product management (or equivalent), with 3+ years owning a complex B2B platform or data product end-to-end. Domain expertise in consumer or SME lending with exposure to digital underwriting and LOS ecosystems. Proven record shipping MLor rules-driven decisioning products with measurable business impact (e. g., approval lift, loss reduction, Ops efficiency). Strong product discovery toolkit (user research, JTBD, problem framing, experiment design) and exceptional written communication (clear specs, narratives, and stakeholder docs). Analytical fluency comfortable with metrics, dashboards, and basic SQL/BI; able to define instrumentation and experimentation. Technical depth to collaborate on APIs, data models, scoring policies, and system trade offs (no need to code, but you can reason about complexity and risk). Experience leading cross-functional teams and resolving complex prioritization across executives, GTM, Delivery/PS, and Engineering. Nice To Have Experience with model governance and regulatory explainability in credit (e.g., reason codes, adverse action workflows). Background in pricing/packaging and commercial strategy for enterprise SaaS. Platform/ecosystem chops (developer docs, SDKs, integration frameworks) (ref:hirist.tech)
The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. Responsibilities Analyze raw data: assessing quality, cleansing, structuring for downstream processing Design accurate and scalable prediction algorithms Collaborate with engineering team to bring analytical prototypes to production Generate actionable insights for business improvements Qualifications Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) At least 1 - 2 years' of experience in quantitative analytics or data modeling Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms Fluency in a programming language Python, Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)
As the Lead AI Platform Engineer at our organization, you will play a crucial role in architecting and implementing the foundational elements of our Agentic AI Platform. Your responsibilities will include designing, deploying, and maintaining production-grade multi-agent systems leveraging frameworks like CrewAI, LangGraph, or custom-built orchestration layers. You will collaborate closely with data engineers, data scientists, ML engineers, and product teams to create adaptive, reasoning-driven systems. - Architect & Build Agentic Systems: Design, develop, and deploy multi-agent workflows using frameworks such as CrewAI, LangGraph, or custom-built orchestration layers. - Platform Engineering: Construct the agentic runtime, including message routing, memory management, and context-sharing mechanisms between agents. - LLM & Tool Integration: Integrate Large Language Models, vector databases, retrieval systems, and external APIs for agent tool-use and reasoning capabilities. - Workflow Design & Optimization: Collaborate with AI researchers and solution engineers to design dynamic agent workflows that adapt based on contextual information and analytical results. - Cloud & Scalability: Architect scalable deployments on AWS, GCP, or Azure, leveraging cloud-native components for high availability and performance. - Observability & Governance: Implement monitoring, evaluation metrics, and safety checks for autonomous agents to ensure reliability and compliance. - Team Leadership & Mentorship: Provide guidance to a team of engineers, establish best practices, and build high-performance engineering culture. Required Qualifications: - Strong background in Python with experience in asynchronous programming, API development, and distributed systems. - Proven experience in building LLM-powered multi-agent systems using frameworks like CrewAI, LangGraph, or similar. - Deep understanding of prompt engineering, RAG pipelines, and tool-calling mechanisms. - Hands-on experience with cloud infrastructure and MLOps components. - Solid understanding of state management, context persistence, and memory architecture for agents. - Experience integrating vector stores and LLM APIs. - Ability to architect scalable AI systems from prototype to production. - Excellent communication and collaboration skills. - Experience with Graph-based orchestration or LangGraph advanced workflows. - Familiarity with agent coordination frameworks like CrewAI. Nice-to-Have: - Exposure to event-driven architectures, message queues, or knowledge graphs. - Understanding of AI safety, alignment, and governance principles. - Contributions to open-source agent frameworks or AI orchestration tools. - Ability to customize models to specific purposes. In addition to working on cutting-edge technology and being part of a collaborative, innovation-driven culture, we offer competitive compensation, potential equity options, and the opportunity to build the next-generation Agentic AI Platform with real-world impact.,