78 Model Monitoring Jobs

As a potential candidate for this position, you will be responsible for contributing to cutting-edge AI/ML solutions at Goldman Sachs. Here is a breakdown of the qualifications and attributes you should possess: - A Bachelor's, Master's or PhD degree in Computer Science, Machine Learning, Mathematics, or a related field is required. - Preferably 7+ years of AI/ML industry experience for Bachelors/Masters, 4+ years for PhD, with a focus on Language Models. - Strong foundation in machine learning algorithms, including deep learning architectures like transformers, RNNs, CNNs. - Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn. - Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques. - Experience working with embedding models and vector databases. - Experience with MLOps practices, including model deployment, containerization (Docker, Kubernetes), CI/CD, and model monitoring. - Strong verbal and written communication skills. - Curiosity, ownership, and willingness to work in a collaborative environment. - Proven ability to mentor and guide junior engineers. Desirable experience that can set you apart from other candidates includes: - Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real-world problems. - Understanding of scalability and performance optimization techniques for real-time inference such as quantization, pruning, and knowledge distillation. - Experience with model interpretability techniques. - Prior experience in code reviews/architecture design for distributed systems. - Experience with data governance and data quality principles. - Familiarity with financial regulations and compliance requirements. About Goldman Sachs: At Goldman Sachs, the commitment is to help clients, shareholders, and communities grow by leveraging people, capital, and ideas. Established in 1869, Goldman Sachs is a prominent global investment banking, securities, and investment management firm headquartered in New York with offices worldwide. Goldman Sachs is dedicated to fostering diversity and inclusion by providing numerous opportunities for personal and professional growth, including training, development, networks, benefits, wellness, personal finance offerings, and mindfulness programs. To learn more about the culture, benefits, and people at Goldman Sachs, visit GS.com/careers. Goldman Sachs is dedicated to providing reasonable accommodations for candidates with special needs or disabilities during the recruiting process. To learn more about accommodations, visit: https://www.goldmansachs.com/careers/footer/disability-statement.html Copyright The Goldman Sachs Group, Inc. 2023. All rights reserved.,

As a highly skilled MLOps Engineer at UPS, you will design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). Your responsibilities will include automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. Your role will involve collaborating with technical teams and business stakeholders to develop cutting-edge machine learning systems that add value to the business. Responsibilities: - Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. - Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. - Troubleshooting and resolving problems, maintaining documentation, and managing model versions for audit and rollback. - Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. - Optimization of queries and pipelines. - Modernization of applications whenever required. Qualifications: - Expertise in programming languages like Python, SQL. - Solid understanding of best MLOps practices and concepts for deploying enterprise-level ML systems. - Understanding of Machine Learning concepts, models, and algorithms including traditional regression, clustering models, and neural networks (including deep learning, transformers, etc.). - Understanding of model evaluation metrics, model monitoring tools, and practices. - Experience with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. - Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. - Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. - Bachelor's Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: - Experience in Azure MLOPS. - Familiarity with Cloud Billing. - Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. - Experience working in an Agile environment, understanding of Lean Agile principles. UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.,

As an MLOps Engineer at our company, you will play a crucial role in building, deploying, and maintaining machine learning models in production using Google Cloud Platform (GCP). You will collaborate closely with data scientists and engineers to ensure the reliability, scalability, and performance of our ML systems. Your expertise in software engineering, data engineering, and machine learning will be utilized to automate ML pipelines, monitor model performance, and troubleshoot any issues that arise. This is an exciting opportunity to work on cutting-edge ML projects and have a significant impact on our business. Key Responsibilities: - Design, develop, and maintain scalable and reliable MLOps pipelines on GCP - Automate the deployment and monitoring of machine learning models in production - Collaborate with data scientists to productionize their models and experiments - Implement CI/CD pipelines for machine learning models - Monitor model performance and identify areas for improvement - Troubleshoot and resolve issues related to ML infrastructure and deployments - Optimize ML pipelines for performance and cost efficiency - Develop and maintain documentation for MLOps processes and infrastructure - Stay up-to-date with the latest MLOps tools and techniques - Ensure compliance with security and data privacy regulations Required Skills & Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field - 3+ years of experience in MLOps or a related role - Strong proficiency in Python and PySpark - Extensive experience with Google Cloud Platform (GCP) services, including BigQuery, Airflow, and Dataproc - Experience with containerization technologies such as Docker and Kubernetes - Experience with CI/CD pipelines and automation tools - Solid understanding of machine learning concepts and algorithms - Experience with model monitoring and alerting tools - Excellent problem-solving and communication skills - Ability to work independently and as part of a team - Experience with infrastructure-as-code tools such as Terraform or CloudFormation is a plus (Note: Additional Information section is omitted as it is not provided in the job description),

The successful candidate will join the Model Risk Management and Validation Group (MRM&V). MRM&V performs independent analytical reviews and validations of all models throughout Bank. The candidate will perform analysis of quantitative models and other tools through extensive reviews into how the models was developed and how it functions. The candidate will also provide written communications of their findings in a clear, succinct manner to all model stakeholders including model owners, developers, lines of business, audit and regulatory agencies. By the nature of this position, the successful candidate will be exposed to a wide variety of models and business analysts, and senior management. Responsibilities: Perform independent review and model validation of Artificial Intelligence and Machine Learning (AI/ML) models; Conduct model architecture review, theoretical soundness assessments and performance evaluations; Write validation reports containing/explaining the analyses performed and their results; Present the reports and findings to various review/approval committees and to other modeling teams; Develop creative approaches to effectively communicate complicated concepts, model validation results and analyses to stakeholders; Participate in peer brainstorm review sessions and help other MRM&V members to solve the problems they are facing; work closely with cross-functional teams, including senior model validators, model developers, risk officers, and business units, to ensure models are effectively integrated into business decision-making processes. Represent Model Risk management team in the interactions with regulatory agencies. Qualifications 3 or more years experiences in model development or validation. Familiar with essential quantities techniques used in financial models. Quantitative programming skill (e.g., SAS/SQL, Python, R, etc.). Knowledge with Machine Learning algorithm in model development or validation is a plus. Very good communication skills (both verbal and written) as well as solid project management skills and ability to multitask. Good business knowledge and familiarity with consumer/small business/commercial banking products, operation, and credit processes. Prior experience with developing or validating models such as stress testing, prepayment or profitability models will be a plus. Prior experience in delivering both written and verbal communications to a senior management audience and developing constructive relationships with a wide range of different stakeholders. Education: Masters or Ph.D. degree in finance, economics/econometrics, statistics, or other quantitative fields (physics, computer science, mathematics, etc.) Please note that we are looking for only Immediate to 30 days joiners

The Opportunity As the Global Practice Lead AI, you will lead the global strategy, development, and delivery of AI solutions centered around Nutanix Enterprise AI (NAI). This role demands deep expertise in generative AI, LLMOps, and hybrid cloud architectures, with a strong focus on operational simplicity, scalability, and enterprise-grade security. You will drive innovation, build high-performing practice team spanning across geos, and ensure successful client outcomes across industries. The Global Practice Lead for AI is expected to bring a strong foundation in deep expertise in delivering enterprise-grade AI solutions across industries especially in terms of the infrastructure needs in AI. Expertise in machine learning, deep learning, generative AI, NLP and cloud-based AI platforms with working knowledge of Kubernetes is expected. As the Global Practice Lead AI, you will be responsible for building, scaling, and leading the AI practice globally. You will define the strategic direction, develop service offerings, drive innovation, and ensure delivery excellence across engagements. This role requires deep technical expertise in AI technologies, strong business acumen, and proven leadership in managing global teams and client relationships. The role focuses on thought leadership, pre-sales, and intellectual property creation in the arena of an cloud and automation consulting practice. Additionally, the Practice contributes to the optimization of service delivery and delivery processes. You will not be managing consultant resources, the scheduling of customer delivery jobs, or the financial aspects of revenue attainment from delivery. Your Role Work with Services Product Management to develop the catalog of service offerings delivered by both Nutanix and Partner consultants. Lead the development of service SKUs, delivery toolkits, and level or effort for all products in the portfolio. Develop repeatable best practices and design guides based upon common customer experiences and solutions. Work with the Services Practice Development team to keep items current and develop training programs to enable the selling and delivery of services. Evangelize Nutanixs vison for hybrid and Enterprise Cloud and software defined datacenter. Develop yearly training plans for practice consultants and track attainment of the training goals. Review statements of work and be responsible for validating services level of effort in the pre-sales process, on AI services offerings. In larger pursuits, work Services Sales Managers and Architects to construct class-leading, complex global outcomes. Provide technical presales expertise on complex and/or custom professional services offerings and solutions. Develop customer trends and common use cases for our product and services, especially within areas of subject matter expertise. Work with Nutanix customers to understand business goals and translate them into technical solutions based upon Nutanix product. Work with Nutanix Product Managers and Enterprise Architects to create service offerings that properly map back to new and existing consulting product offerings. Working closely and foster relationships with key clients, Product Management, Product Engineering, and the Sales and SE organizational leaders. Drive and manage KPI for practice consulting attach and incremental corporate value. Lead and refine opportunity plans and grow the surface area for campaigns aligning with the corporate mission. Participate in sales and revenue forecasts, quarterback creative ways to provide customer value, and stay abreast of status/details for each campaign and opportunity where your involvement guarantees a successful outcome. Monitor practice consulting CSAT, delivery quality, and follow up with white glove service on deficient areas where necessary. Monitor practice consulting offer delivery margin and adherence to expected level of effort for standard offerings and take corrective action when necessary. Deliver Platform consulting solutions to key Clients and projects where necessary. The expected billable percentage is 20%. What You Will Bring 15+ years of experience in data driven IT services with at least 3 years AI technologies. Proven experience in practice management and delivering enterprise-grade AI solutions across multiple industries. Proven experience in deploying GenAI solutions using Kubernetes, LLM APIs, and hybrid cloud platforms. Expertise in LLMOps, model monitoring, RBAC, and secure API workflows. Familiarity with Nutanix Cloud Infrastructure, Kubernetes Platform, and Unified Storage will be a big plus. Expertise in machine learning, deep learning, generative AI, NLP, computer vision, and cloud-based AI platforms. Strong understanding of AI ethics, governance, and regulatory frameworks. Working knowledge of Kubernetes, containers, CI/CD, DevOps, and cloud platforms (AWS, Azure, GCP). Strong understanding of enterprise architecture, application modernization, and agile delivery. Excellent leadership, communication, and stakeholder management skills. Bachelors or masters degree in computer science, Engineering, or related field; certifications in AI, analytics, data, cloud-native technologies are a plus. Excellent understanding of hybrid cloud modalities, IT go-to market challenges, and business drivers that impact customer purchasing decisions Proven experience in designing AI solutions based upon customer requirements, hosted on cloud architecture in a Premier account portfolio Natural leadership abilities, ability to thrive in an independent manner combining exceptional interpersonal communication, virtual team building, and presentation skills Ability to whiteboard solutions interactively with customers and develop robust architectural documentation Ability to analyze contractual and commercial constructs of a services engagement, Enterprise Purchase Agreements (EPA), working with extended account teams in a potentially multi-vendor arrangement Strong communications skills, both verbal and written, with the ability to engage and win the confidence of C-Level executives, Geo leads, Sales Managers, Enterprise Architects and Service Delivery Managers. Work Arrangement Hybrid: This role operates in a hybrid capacity, blending the benefits of remote work with the advantages of in-person collaboration. For most roles, that will mean coming into an office a minimum of 3 days per week, however certain roles and/or teams may require more frequent in-office presence. Additional team-specific guidance and norms will be provided by your manager.

Role Overview We have a challenging opportunity for the aforementioned roles in our Financial Services practice. The person will focus on Indian and global clients, work in a client-facing role, and take on the responsibility of delivering and leading projects around Credit risk analytics and or providing a single point end-to-end accountability for the project oversight, establish a working relationship with the internal and external stakeholders. In line with the increasing regulatory requirements within different aspects of Enterprise Risk Management, the candidate would support banks in Enterprise Risk Management Support catering to changing regulations, review and oversight of credit risk models and with a view to keep its existing ERM framework in speed with the regulatory requirements and long term strategy of the bank. Key Responsibilities Preforming Risk Analytics activities to develop models and support the bank on various analytical initiatives Assist in modeling key risk estimates PD, LGD and EAD for AIRB and IFRS9 framework Regularly engage in model development, validation, and re-development activities Other risk analytics activities include assisting in review and re-development of Macro-Economic Model, RAROC Calculator Risk Adjusted Return on Capital Period reporting (internal & regulatory) of various Risk Metrics Engage in model risk management activities Desired Profile Postgraduate with 7-10 years of experience in the Banks, NBFCs, consulting firms Certificates like CFA, FRM, CQF Should be proficient in MS Excel and PowerPoint Excellent knowledge of SAS, R, Python. Should have excellent communication skills (oral, written, and email drafting skills) Good organizational, analytical, problem-solving, and project management skills Technical Knowledge Understanding and experience in credit risk function, specifically retail models. Understanding of banking products, operations, and strong knowledge of Basel and IFRS 9 regulatory landscape and regulations in risk, capital, operation, and compliance. Prior work experience with regulators in India and Middle East (RBI, CBUAE, SAMA etc.) is preferred Deep understanding and strong knowledge of SAS/R/Python Understanding of retail banking, corporate banking, capital markets, trading, and other financial services. Individual must have experience in IFRS9, Basel II, III and IV Standardized and Advanced approaches, BCBS, ICAAP, Stress testing, Scorecard development, policies/ procedures, system implementation, etc. Experience in developing PD, LGD, EAD and CCF models for banks and financial institutions Experience in statistical methods such as logistic and Probit regressions Experience in macroeconomic model development and stress testing Key Personal Attributes A good blend of creative thinking and rigorous analysis in solving business problems Strong communication, facilitation, relationship-building, presentation, and negotiation skills Must work well in a team-oriented environment as well as independently. Work with team members to set goals and responsibilities for specific engagements. Foster teamwork and innovation. Ability to work under pressure. Mature, proactive, and displays initiative. Manages own and others' time well. Good oral and written communication skills including documentation of findings and recommendations. Adept at preparing and presenting reports to an audience.

Location -Gurgaon Time 1 pm to 10pm Both sides cab available Looking for candidate who can develop models related to credit Risk Model, FICO Model. Ensure models are accurately tuned and meet regulatory and business requirements. Prepare detailed reports and presentations on fraud trends, model performance, and recommendations for improvement. Communicate findings to stakeholders and senior management effectively. Create and maintain comprehensive Model Development Reports (MDRs) summarizing validation activities, methodologies, and results. Conduct thorough assessments of financial crime models to identify strengths, weaknesses, and areas for improvement. Perform detailed data analysis to evaluate the accuracy and reliability of fraud-related findings. Identify any discrepancies or anomalies in the data provided by financial crime vendors. Ensure all documentation adheres to regulatory and organizational standards. Collaborate with model development, and compliance teams to address any concerns or issues identified during the validation process. Stay abreast of industry best practices and regulatory changes related to credit reporting and scoring Bachelors/ masters degree in Statistics, Economics or a related field (FRM is a plus). 1 -5 years of experience in Model validation and monitoring of Fraud/Financial crime models. Familiarity with fraud detection systems and vendors such as FICO, VISA and STAR is a plus. Hands-on experience in programming languages like SQL, Python and advanced excel. Excellent communication and interpersonal skills. Please share your profile at Surbhi.malhotra@nlbtech.com

Develop and implement a robust and scalable AI/ML architecture that aligns with business objectives. Design, develop architect scalable and robust AI/ML solutions and help productionize the workloads. Define and enforce AI/ML architecture, standards and best practices including MLOps. Help in accuracy and robustness improvements for AI systems which have been developed. Implement model monitoring and performance optimization strategies. Ensure model scalability and reliability in production environments. Enable innovation, efficiency and competitive advantage through AI/ML. Cost-effectiveness of the AI/ML infrastructure. Define the overall AI /ML architecture roadmap and patterns practices. Research and analysis to identify emerging AI architecture, techniques and accuracy improvement trends and patterns and on the latest advancements in AI/ML. Drive AI /ML /MLOps architecture definition and guidance on developing solutions. Maintain a competitive edge and explore new business opportunities Contribute to Infra architecture for ML model training and deployment. Prototype and experiment with novel AI/ML techniques and applications. Define the overall AI architecture roadmap and patterns, MLOPs practices. Work on prototypes Successful implementation of new techniques. Drive innovation and research in AI/ML to identify and implement cutting-edge solutions. Conduct regular audits and assessments of solutions. Put together Auditing framework with various aspects on scoring method and execute. Compliance with ethical guidelines and regulations. Reduction in bias and fairness issues in models. Conduct code reviews and architecture assessments and evaluations using standard techniques and best practices. Develop and publish Evaluation Metrics Design and put together AI auditing framework Evaluate and select appropriate AI models and frameworks and help in evaluation POCs. Contribute to presentation, content, awareness building and trainings of AI/ML. Presentation and content development Awareness building and communication Training and capability building Metrics and evaluation.

Senior MLOps Engineer As a Senior MLOps engineer, together with Pattern's Data Science and Engineering teams, you will create and maintain impactful solutions for our brands across the world. From traditional machine learning to cutting-edge AI, you will work and lead throughout the model lifecycle. Responsibilities: Teamwork: MLOps is a team sport, and we require a contributor who can elevate everyone in the MLOps organization. While technical skills are required, your communication and teamwork skills will deliver tangible value to our teams as well as elevate the teams capacity. Collaboration: Directly work with data science, engineering, and machine learning teams around the world, including evening IST. Pipeline Management: Architect, implement, and maintain scalable ML pipelines, with seamless integration from data ingestion to production deployment. Model Monitoring: Lead the operationalization of machine learning models, ensuring hundreds of models are continuously monitored, retrained, and optimized in real-time environments Deployment: Deploy machine learning platform solutions in the cloud, securely and cost effectively. Reporting: Effectively communicate actionable insights across teams using both automatic (e.g., alerts) and non-automatic methods. The type of game-changing candidate we are looking for: Hungry: The position is for those who want to become an MLOps thought leader, mastering and incorporating the latest best practices into an ML platform? Transparent: Willingness to identify and admit errors and seek out opportunities to continually improve both in their own work and across the team. Clear Communicator: MLOps is a central node in a complex system. Clear, actionable, and concise communication, both written and verbal is a must. Solution-Oriented: We focus on consistently delivering the best solutions with a solutionsoriented positive attitude. Strong demonstration of technical expertise in Computer Science, Machine Learning, Data Science, or a related field Multiple years of direct and extensive experience with AWS Multiple years of experience building and managing MLOps platform tools Excited to empower DS with tools, practices, and training that simplify MLOps enough for Data Science to increasingly practice MLOps on their own and own products in production.

Role: Data Scientist (Optimization Real-Time Ad Marketplace) A fast-growing startup in the digital advertising space is seeking a Data Scientist focused on optimization strategies for a real-time ad marketplace. In this role, you will design experiments to better understand bidding behaviors, improve algorithmic performance, and optimize outcomes for publishers, DSPs, and the platform alike. You will contribute to building proof-of-concept models, deploying ML models into production, creating reusable features and data structures, and collaborating with cross-functional teams to advance data science initiatives. Responsibilities: Research and experiment to improve bidding algorithm performance and maximize results for customers. Develop new bidding and pricing optimization strategies, build proof-of-concept ML models, and scale them to production. Partner with Product, Engineering, and other teams to deliver analytics projects and influence product strategies. Monitor and measure ML/statistical model performance, creating dashboards and alerts to ensure reliability. Build reusable modules, data structures, and provide guidance/feedback to team members. Qualifications: Bachelors degree or higher in a quantitative field (Mathematics, Computer Science, Engineering, Economics, Operations Research, etc.). 2+ years of professional experience in data science and machine learning . Familiarity with tools such as Python, Spark, DataBricks, ONNX, MySQL, Snowflake, Airflow, Docker, and AWS . Experience with ML libraries like scikit-learn for analysis and prototyping models. Knowledge of monitoring/model performance tools such as Prometheus, Grafana, Looker . Strong analytical, problem-solving, and technical communication skills. Commitment to continuous learning and sharing expertise with team members. Technologies in Use: Languages: Python, Java Frameworks: Spark, DataBricks, ONNX, Docker, Airflow Databases: MySQL, Snowflake, S3/Parquet Cloud: Amazon Web Services Thanks & Regards, Gloria Dias Research Associate | LH persolindia.com Pune, India CONFIDENTIAL NOTE: This e-mail and any attachments may contain confidential information. If you are not the intended recipient, please notify the sender immediately and delete this message. Unauthorized use or distribution of this communication is strictly prohibited. By submitting your curriculum vitae or other personal data to us in connection with your job application or in your capacity as our employee, contractor, associate, partner or vendor, you acknowledge that you have carefully read and agreed to the terms of our Privacy Policy and the consent notice thereunder. You hereby provide voluntary consent to the collection, use, processing and disclosure of your personal data by us and our affiliates, in accordance with and for the purposes set out in our Privacy Policy and for other legitimate purposes as specified under applicable law. Your submission of personal data via email implies that you have not expressly dissented to the processing of personal data for the stated purpose. For a detailed understanding of our data collection practices, please refer to our Privacy Policy accessible here. If at any time, you wish to expressly withdraw your consent or have any grievance, you can do so by submitting a request to our designated consent manager, as provided in our Privacy Policy. Your privacy is of utmost importance, and we are committed to address the queries you have in this regard. SECURITY NOTE: We at PERSOL India or our representatives, do not ask job seekers for fees, personal banking information, or payments through unofficial channels. Official communications will only come from @persolapac.com. Report any suspicious activity to Contactus.in@persolapac.com. Click here to find out how you can safeguard yourself from job scams.

You should have expertise in ML/DL, model lifecycle management, and MLOps tools such as MLflow and Kubeflow. Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models is essential. You must possess strong experience in NLP, fine-tuning transformer models, and dataset preparation. Hands-on experience with cloud platforms like AWS, GCP, Azure, and scalable ML deployment tools like Sagemaker and Vertex AI is required. Knowledge of containerization using Docker and Kubernetes, as well as CI/CD pipelines, is expected. Familiarity with distributed computing tools like Spark and Ray, vector databases such as FAISS and Milvus, and model optimization techniques like quantization and pruning is necessary. Additionally, you should have experience in model evaluation, hyperparameter tuning, and model monitoring for drift detection. As a part of your roles and responsibilities, you will be required to design and implement end-to-end ML pipelines from data ingestion to production. Developing, fine-tuning, and optimizing ML models to ensure high performance and scalability is a key aspect of the role. You will be expected to compare and evaluate models using key metrics like F1-score, AUC-ROC, and BLEU. Automation of model retraining, monitoring, and drift detection will be part of your responsibilities. Collaborating with engineering teams for seamless ML integration, mentoring junior team members, and enforcing best practices are also important aspects of the role. This is a full-time position with a day shift schedule from Monday to Friday. The total experience required for this role is 4 years, with at least 3 years of experience in Data Science roles. The work location is in person. Application Question: How soon can you join us ,

As an AI Architect with relevant experience in Natural Language Processing (NLP), Computer Vision (CV), and Large Language Models (LLMs), you will have the responsibility of designing, building, and fine-tuning NLP models and LLM agents to address various business challenges. Your role will involve creating innovative and efficient model designs that elevate user experiences and streamline business processes. Strong design skills, hands-on coding expertise, advanced proficiency in Python development, specialized knowledge in LLM agent design and development, as well as exceptional debugging capabilities are essential for this position. Responsibilities: - Model & Agent Design: You will conceptualize and design robust NLP solutions and LLM agents customized to meet specific business requirements, emphasizing user experience, interactivity, latency, failover, and functionality. - Hands-on Coding: Composing, testing, and maintaining clean, efficient, and scalable code for NLP models and AI agents using Python programming will be a core aspect of your role. - Build high-quality multi-modal & multi-agent applications/frameworks. - Performance Monitoring: Monitor and optimize LLM agents, implement model explainability, manage model drift, and ensure robustness. - Research Implementation: Implement AI Agent research papers into practical solutions, staying updated with the latest academic and industry research to apply cutting-edge methodologies. - Debugging & Issue Resolution: Proactively identify, diagnose, and resolve issues related to AI agents, including model inaccuracies, performance bottlenecks, and system integration problems. - Innovation and Research: Experiment with new techniques and tools to enhance agent capabilities and performance, staying abreast of advancements in AI agent technologies. - Continuous Learning: Adapt to new technologies and practices in the ML field, maintaining awareness of emerging trends in Agent-based solutions, especially in Text & Multi-Modal contexts. Education Qualifications: Bachelor's / Master's degree in Engineering Required Skills: - Programming languages: Proficiency in Python. - Public Cloud: Familiarity with Azure. - Frameworks: Experience with Vector Databases like Milvus, Qdrant/ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation, and Observability Tools. Understanding of Guardrails strategy for LLM. Knowledge of Arize or other ML/LLM observability tools. - Experience: Demonstrated experience in building functional platforms using ML, CV, LLM platforms, and evaluating/monitoring AI platforms in production. Skills: LLM agent design, Python, Artificial Intelligence, Guardrails strategy, Model monitoring, ML, AI Orchestration, Performance optimization, AI evaluation, Observability tools, NLP, Embedding models, Debugging, Multi-modal applications, Research implementation, Vector databases.,

Join us as an Assistant Vice President Model Risk Audit at Barclays, where you will spearhead the evolution of our digital landscape, driving innovation and excellence. You will harness cutting-edge technology to revolutionize our digital offerings, ensuring unparalleled customer experiences. You may be assessed on the key critical skills relevant for success in the role, such as experience with modeling, model monitoring, model validation, model risk management, and model risk audit role, as well as other job-specific skill sets. To be successful as an Assistant Vice President Model Risk Audit, you should have experience with: Basic/ Essential Qualifications: - Prior experience in model risk management or models related to traded products, financial derivatives, credit risk, counter-party credit risk, fraud risk, climate risk, or Artificial Intelligence/Machine Learning. - Excellent communication skills. Ability to communicate effectively and have difficult conversations with senior stakeholders (technical and non-technical). - Strong written skills and meticulous adherence to detail. - Quantitative postgraduate/Engineering (B Tech) degree or professional qualification (CFA, ACA, FRM, FIA, CAIA, MBA etc). Desirable skill sets/ good to have: - Demonstrated project management experience and ability to conduct complex work under pressure. - Proven working experience and exposure on end-to-end audits. - Experience in leading others in large projects/initiatives/audit assignments. - Strong risk and control understanding. - Knowledge of one or more programming languages (Python, SAS, R). - Proven track record of high performance in previous roles. - Knowledge about new and emerging financial products and services. - Display willingness and initiative to learn and share knowledge. This role will be based out of Pune. Purpose of the role: To support the development of audits aligned to the bank's standards and objectives by working collaboratively with colleagues, providing accurate information and recommendations, and complying with policies and procedures. Accountabilities: - Audit development and delivery support, including financial statements, accounting practices, operational processes, IT systems, and risk management. - Identification of operational risks to support the delivery of the Barclays Internal Audit (BIA) Audit Plan through risk assessments. - Assessment of internal control effectiveness and their capability to identify and mitigate risk aligned to regulatory requirements. - Communication of key findings and recommendations to stakeholders, including the Audit Owner, senior managers, and directors. - Identification of regulatory news and industry trends/developments to provide timely insight and recommendations for best practice. Assistant Vice President Expectations: To advise and influence decision-making, contribute to policy development, and take responsibility for operational effectiveness. Collaborate closely with other functions/business divisions. Lead a team performing complex tasks, using well-developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives, and determination of reward outcomes. If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviors to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviors are: Listen and be authentic, Energize and inspire, Align across the enterprise, Develop others. OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialization to complete assignments. They will identify new directions for assignments and/or projects, identifying a combination of cross-functional methodologies or practices to meet required outcomes. Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues. Identify ways to mitigate risk and develop new policies/procedures in support of the control and governance agenda. Take ownership for managing risk and strengthening controls in relation to the work done. Perform work that is closely related to that of other areas, which requires an understanding of how areas coordinate and contribute to the achievement of the objectives of the organization sub-function. Collaborate with other areas of work, for business-aligned support areas to keep up to speed with business activity and the business strategy. Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practices (in other areas, teams, companies, etc.) to solve problems creatively and effectively. Communicate complex information. "Complex" information could include sensitive information or information that is difficult to communicate because of its content or its audience. Influence or convince stakeholders to achieve outcomes. All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence, and Stewardship our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset to Empower, Challenge, and Drive the operating manual for how we behave.,

Role & responsibilities : This role manages a team of machine learning engineers responsible for the development and programming of machine learning integrated software algorithms. Supports the continuous evolution and strategy of Vanguard's machine learning engineer program. This role also includes creating data mining architectures, applying data science methodologies, and researching emerging techniques to identify trends and inform business decisions. Preferred candidate profile : Hires, evaluates, and supervises crew. Provides guidance and training as necessary to develop crew. Sets performance standards, reviews performance, and makes informed compensation decisions in accordance with all applicable Human Resources policies and procedures. Oversees complex data pipelines and implements data engineering design principles for iterative data pipeline development to drive scale and efficiency. Monitors existing data and model pipelines in a production environment. Identifies and diagnoses data inconsistencies and errors, documents assumptions, and forages to fill data gaps. Applies knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning concepts to create self-running artificial intelligence (AI) systems to automate predictive models. Collaborates with other data science teams to review model ready dataset document/feature documentation. Partners with data science teams to understand data requirements. Oversees detailed analysis of raw data sources for data quality, applies business context, and model development needs. Drives efficiency through the use of data discovery tools. Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Supports ongoing business planning and departmental prioritization activities. Guides model monitoring scripts as needed. Diagnoses root causes based on model monitoring alerts and triages issues. Coordinates and plans response to model monitoring alerts and resolves issues. Executes operational plans to achieve department objectives. Collaborates with department or functional heads in planning, developing, and executing near-term goals. Prepares, reviews, and delivers insight presentations and action recommendations. Communicates complex analytical findings and implications to business leaders. Participates in special projects and performs other duties as assigned. Develops scalable data mining strategies and analytical models to uncover hidden patterns and improve decision-making processes across domains. Applies domain-specific data analysis techniques in sectors like marketing, supply chain, or scientific research to deliver strategic impact. Explores and integrates emerging data science frameworks and tools to enhance analytical maturity and model performance across the enterprise.

As the Head of Data at our Bengaluru (Bellandur) location, you will be responsible for leading the design, development, and optimization of ETL/ELT pipelines to process structured and unstructured data. You will play a key role in maintaining a modern data warehouse/lakehouse architecture, implementing data partitioning, indexing, and performance tuning strategies, and ensuring robust cataloging, data quality rules, validation checks, and monitoring systems are in place. Additionally, you will define appropriate data privacy and security guidelines and take ownership of business metrics from extracting and exploring data to evaluating methods and results rigorously. Collaborating with stakeholders, you will define and implement key business metrics, establish real-time monitoring for critical KPIs, and build and maintain BI dashboards automation with consistency and accuracy in reporting. Your role will involve performing A/B testing, generating and validating business hypotheses, designing analytical frameworks for product funnels and biz ops, and empowering business teams with self-service reporting and data crunching. Furthermore, you will be responsible for building predictive models such as churn prediction, demand forecasting, route planning, and fraud detection. Developing Advanced ML solutions like recommendation systems, image processing, and Gen AI applications will be part of your responsibilities. You will deploy machine learning models into production, monitor models, update them, and ensure proper documentation. In collaboration with product managers, engineering, risk, marketing, and finance teams, you will align analytics with business goals. Building and mentoring a high-performing analytics team, defining the analytics vision and strategy, and translating it into actionable roadmaps and team initiatives will be crucial. Presenting actionable insights to senior executives and stakeholders, solving ambiguous problems through a structured framework, and executing high-impact work are key aspects of this role. To qualify for this position, you should have 10-12 years of experience with at least 6 years in delivering analytical solutions in B2C consumer internet companies. A Bachelor's Degree in a data-related field such as Industrial Engineering, Statistics, Economics, Math, Computer Science, or Business is required. Strong expertise in Python, R, TensorFlow, PyTorch, and Scikit-learn, as well as hands-on experience with AWS, GCP, or Azure for ML deployment, is essential. Proven experience in Power BI report development, ETL pipelines, data modeling, and distributed systems is also necessary. Expertise in supply chain processes and demand forecasting, along with skills in time series models and regression analysis, will be beneficial. Strong documentation skills and the ability to explain complex technical concepts to non-technical personnel are also required for this role.,

Exp: Credit Risk Model Development mandatory Min: 2-8 Yrs MBA Finance preferred, FRM/CFA a plus Location: Bangalore | 5 Days WFO(ODC Set-up) Salary: Depends on last drawn NP: 30Days or Immediate Joiners Please drop cv on karishmasharma@imaginators.co Required Candidate profile Credit Risk expert with exp. in Model Development or Validation (IFRS9/IRB/CCAR/CECL) Skilled in SAS, SQL, Python, R. MBA Finance, FRM/CFA preferred.

You will be responsible for monitoring credit risk IRB and IFRS9 models for the measurement of PD, EAD, and LGD for the bank's retail portfolios. Your tasks will include working on the end-to-end model monitoring cycle, starting from data gathering and cleansing to documentation and presentations to key stakeholders. You are expected to enhance the current monitoring outputs by adding analytical insights and ensuring that all monitoring results and analysis comply with the Group monitoring standards. You will need to understand model-related uncertainty risks such as data, regulatory, and business strategy that directly impact the models" performance. It is crucial to ensure that the monitoring process and models meet the Bank's Model Risk Policy and Model Family Standards. In terms of regulatory and business conduct, you are expected to display exemplary behavior and adhere to the Group's Values and Code of Conduct. You must take personal responsibility for upholding the highest standards of ethics and compliance with all applicable laws, regulations, guidelines, and the Group Code of Conduct. Collaborating with key stakeholders such as Group Model Validation, Model Sponsors and Owners, Model Risk Management, Internal and External Audit, and Regulators is essential for the role. To qualify for this position, you should have a degree (preferably postgraduate) in a quantitative discipline and possess 3-4 years of experience in a Retail model development/validation/monitoring role. Standard Chartered is an international bank committed to making a positive difference for its clients, communities, and employees. If you are passionate about driving commerce and prosperity through diversity and inclusion, we encourage you to apply and be part of a purpose-driven organization. In addition to a challenging and rewarding career, Standard Chartered offers a range of benefits including core bank funding for retirement savings, medical and life insurance, flexible working options, proactive wellbeing support, continuous learning opportunities, and an inclusive work environment where diversity is celebrated and respected. If you are looking for a meaningful career with a bank that values difference and advocates inclusion, we look forward to receiving your application.,

Join Barclays as Vice President Impairment, where you will help colleagues demonstrate analytical and technical skills, along with knowledge of the fundamentals of retail credit risk management, particularly across impairment management. At Barclays, the future is not just anticipated - it is created. Your role will involve embedding a control functionality by leading the development of the team's output and demonstrating sound judgment in collaboration with the wider team and management. To be successful in this role, you should have experience in owning IFRS9, CCAR, and CECL risk models, managing the entire lifecycle from data governance to monitoring. Additionally, knowledge of presenting findings on risk models, developing Post Model Adjustments (PMA), reviewing model monitoring reports, and designing strategic remediation support is essential. Other highly valued skills may include a good understanding of reviewing and challenging impairment models, team management experience, knowledge of key regulatory requirements, and excellent communication and presentation skills. You may be assessed on key critical skills such as risk and controls, change and transformation, business acumen, strategic thinking, digital and technology, as well as job-specific technical skills. This role is based in our Noida office. Purpose of the role: To evaluate and assess the potential impairment of financial assets, ensuring accurate reflection of the bank's economic value in its financial statements. Accountabilities: Identification of potential impairment triggers, analysis of relevant information, application of quantitative and qualitative impairment tests, assessment of impairment loss, calculation of impairment provision, and management of impaired assets. Vice President Expectations: Advise key stakeholders, manage and mitigate risks, demonstrate leadership and accountability in risk management, understand business functions, collaborate with other areas, create solutions based on analytical thinking, and build trusting relationships with stakeholders. All colleagues are expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence, and Stewardship, as well as the Barclays Mindset of Empower, Challenge, and Drive.,

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Principal Consultant - MLOps Engineer! In this role, lead the automation and orchestration of our machine learning infrastructure and CI/CD pipelines on public cloud (preferably AWS). This role is essential for enabling scalable, secure, and reproducible deployments of both classical AI/ML models and Generative AI solutions in production environments. Responsibilities Develop and maintain CI/CD pipelines for AI/ GenAI models on AWS using GitHub Actions and CodePipeline . (Not Limited to) Automate infrastructure provisioning using IAC. (Terraform, Bicep Etc) Any cloud platform - Azure or AWS Package and deploy AI/ GenAI models on (SageMaker, Lambda, API Gateway). Write Python scripts for automation, deployment, and monitoring. Engaging in the design, development and maintenance of data pipelines for various AI use cases Active contribution to key deliverables as part of an agile development team Set up model monitoring, logging, and alerting (e.g., drift, latency, failures). Ensure model governance, versioning, and traceability across environments. Collaborating with others to source, analyse , test and deploy data processes Experience in GenAI project Qualifications we seek in you! Minimum Qualifications experience with MLOps practices. Degree/qualification in Computer Science or a related field, or equivalent work experience Experience developing, testing, and deploying data pipelines Strong Python programming skills. Hands-on experience in deploying 2 - 3 AI/ GenAI models in AWS. Familiarity with LLM APIs (e.g., OpenAI, Bedrock) and vector databases. Clear and effective communication skills to interact with team members, stakeholders and end users Preferred Qualifications/ Skills Experience with Docker-based deployments. Exposure to model monitoring tools (Evidently, CloudWatch). Familiarity with RAG stacks or fine-tuning LLMs. Understanding of GitOps practices. Knowledge of governance and compliance policies, standards, and procedures Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Model Validation (Credit risk/ Market risk) We are hiring for a leading Financial KPO organization based at Bangalore/Gurugram/ Kolkatta Position : Experience : 8-10 yrs in experience in model validation/ development, quantitative modelling Strong understanding of model risk, validation frameworks, and regulatory requirements. Strong technical skills in python for model development. Education : B.tech/ Masters / MBA - in Economics, Mathematics, Statistics, Finance, Computer science Role & Responsibilities : Responsible for being validator for a wide range of models like IRRBB, credit risk, market risk, counterparty credit risk, fraud detection, Stress Testing, AML and forecasting models Working with independent model validation function of a large banking client and will involve end-to-end validation of risk and regulatory models Independent model validation, especially comprehensive model validation within 2nd line of defense, using SR 11-7 or similar guidelines. Exhaustive model validation will include conceptual assessment of models use, method, assumptions, limitations and on-going monitoring and control, models outcome analysis. Assessment of the model monitoring and implementation process. Assessment of the model calibration techniques Good understanding of vanilla and exotic derivatives in all asset classes, and their impact on various market risk (VaR, SVaR, FRTB SBM, DRC and RRAO) and CCR components. Thorough understanding of stochastic processes and their models, stochastic volatility models, yield curve models Good understanding of conventions of various markets like treasury, fixed income, equities, commodities etc.

We are looking for ML Ops Engineer We are seeking a skilled and proactive ML Ops Engineer to join our team to streamline and scale machine learning workflows. The ideal candidate will be responsible for deploying, monitoring, and maintaining ML models in production environments, ensuring reliability, scalability, and performance. You will work closely with data scientists, software engineers, and DevOps teams to bridge the gap between model development and production deployment. Key Responsibilities: Model Deployment & Automation: Design and implement CI/CD pipelines for ML models. Automate model training, testing, and deployment workflows. Infrastructure & Scalability: Manage cloud-based infrastructure (e.g., AWS, Azure, GCP) for ML workloads. Optimize resource usage and ensure scalability of ML systems. Monitoring & Maintenance: Monitor model performance and data drift in production. Implement logging, alerting, and rollback mechanisms. Collaboration & Integration: Work with data scientists to productionize models. Integrate ML models into existing applications and services. Security & Compliance: Ensure data privacy and compliance with relevant regulations. Implement secure access controls and audit trails. Youd describe yourself as: Required Skills: Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with containerization tools (Docker, Kubernetes). Familiarity with cloud platforms (AWS, GCP, Azure). Knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Understanding of model monitoring tools (e.g., Prometheus, Grafana, MLflow). Strong grasp of software engineering principles and DevOps practices. Preferred Qualifications: Bachelors or Masters degree in Computer Science, Data Science, or related field. Experience with data versioning tools (e.g., DVC). Familiarity with feature stores and model registries. Prior experience in deploying models in real-time or batch environments.

5 years of experience inMLOpsand building ML pipelines Deep understanding of the MLOps lifecycle and automation of ML workflows Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow Strong experience in GPU accelerators and CUDA for model training and optimization Proven experience in model monitoring drift detection and maintaining model accuracy over time Familiarity with CICD pipelines Docker Kubernetes and cloud infrastructure Strong problem solving skills with the ability to work in a fastpaced environment Preferred Qualifications Experience with tools like Evidently AI for model monitoring and drift detection Knowledge of data versioning and model version control techniques Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks Excellent communication and collaboration skills with the ability to work crossfunctionally across teams

Model Validation (Credit risk/ Market risk) We are hiring for a leading Financial KPO organization based at Bangalore/Gurugram/ Kolkatta Position : Experience : 3-8 yrs in experience in model validation/ development, quantitative modelling Strong understanding of model risk, validation frameworks, and regulatory requirements. Strong technical skills in python for model development. Education : B.tech/ Masters / MBA - in Economics, Mathematics, Statistics, Finance, Computer science Role & Responsibilities : Responsible for being validator for a wide range of models like IRRBB, credit risk, market risk, counterparty credit risk, fraud detection, Stress Testing, AML and forecasting models Working with independent model validation function of a large banking client and will involve end-to-end validation of risk and regulatory models Independent model validation, especially comprehensive model validation within 2nd line of defense, using SR 11-7 or similar guidelines. Exhaustive model validation will include conceptual assessment of models use, method, assumptions, limitations and on-going monitoring and control, models outcome analysis. Assessment of the model monitoring and implementation process. Assessment of the model calibration techniques Good understanding of vanilla and exotic derivatives in all asset classes, and their impact on various market risk (VaR, SVaR, FRTB SBM, DRC and RRAO) and CCR components. Thorough understanding of stochastic processes and their models, stochastic volatility models, yield curve models Good understanding of conventions of various markets like treasury, fixed income, equities, commodities etc.

JD_AI/ML Senior Engineer About The Role We are seeking a highly skilled and experienced AI/ML Engineer to join our dynamic team at Medibuddy dedicated to improving healthcare services experience. In this role, you will be instrumental in designing, developing, and deploying cutting-edge artificial intelligence and machine learning solutions that directly impact patient care and operational efficiency. You will leverage your expertise in cloud platforms, large language models, and robust MLOps practices to build scalable and observable AI systems. This position requires a strong advocate for best practices and a mentor who can guide junior engineers, fostering a culture of technical excellence and continuous improvement within the healthcare domain. Responsibilities Champion and advocate for AI/ML best practices, including model governance, ethical AI, data privacy, and responsible AI development. Design, develop, and implement end-to-end AI/ML solutions, from data ingestion and model training to deployment and monitoring. Work extensively with Large Language Models (LLMs), including fine-tuning, prompt engineering, and integration into various applications. Implement comprehensive observability strategies for AI/ML systems, including logging, monitoring, alerting, and performance tracking to ensure system health and model efficacy. Architect and implement solutions for streaming existing software systems into AI/ML pipelines for real-time inference and data processing. Provide technical guidance, mentorship, and support to junior AI/ML engineers, helping them grow their skills and contribute effectively to the team. Collaborate closely with DevOps, software engineers, and product managers to translate business requirements into technical solutions. Conduct research and stay up-to-date with the latest advancements in AI/ML, cloud technologies, and industry trends. Familiarity with AWS cloud services to build, deploy, and manage scalable machine learning infrastructure and applications. Familiarity with developing and maintaining robust MLOps pipelines for continuous integration, continuous delivery (CI/CD), and automated model deployment. Qualifications Bachelor&aposs or Master&aposs degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. 5+ years of professional experience in AI/ML engineering or a similar role. Strong proficiency in Python and relevant ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Demonstrated experience working with Large Language Models (LLMs) and their applications. Hands-on experience with MLOps tools and practices, including CI/CD, version control (Git), and model monitoring. Expertise in setting up and maintaining observability for AI/ML systems (e.g., Prometheus, Grafana, ELK stack). Proven experience with AWS cloud services (e.g., Sagemaker, EC2, S3, Lambda, EKS) for deploying and managing ML workloads. Experience with real-time data streaming technologies (e.g., Kafka, Kinesis) and integrating them with ML pipelines. Excellent communication and interpersonal skills, with a proven ability to advocate for best practices and mentor junior team members. Strong problem-solving abilities and a keen attention to detail. Show more Show less

About Credit Saison India: Credit Saison India, established in 2019, is one of the fastest-growing Non-Bank Financial Company (NBFC) lenders in the country. With verticals in wholesale, direct lending, and tech-enabled partnerships with NBFCs and fintechs, Credit Saison India's tech-enabled model, along with underwriting capability, facilitates lending at scale, addressing India's significant credit gap, especially within underserved segments of the population. Committed to long-term growth as a lender in India, Credit Saison India serves MSMEs, households, individuals, and more. Registered with the Reserve Bank of India (RBI) and holding an AAA rating from CRISIL and CARE Ratings, the company has a branch network of 45 physical offices, 1.2 million active loans, an AUM exceeding US$1.5B, and an employee base of around 1,000 people. As part of Saison International, a global financial company, Credit Saison India aims to bring people, partners, and technology together to create resilient and innovative financial solutions for positive impact. With operations spanning across various countries, including Singapore, India, Indonesia, Thailand, Vietnam, Mexico, and Brazil, Credit Saison India is dedicated to transforming opportunities and enabling people's dreams. Roles & Responsibilities: Define and drive the long-term AI engineering strategy aligned with the company's business goals, focusing on scalable AI and machine learning solutions, including Generative AI. Lead, mentor, and develop a high-performing AI engineering team, fostering innovation, collaboration, and technical excellence. Collaborate with product, data science, infrastructure, and business teams to identify AI use cases, design end-to-end solutions, and seamlessly integrate them into products and platforms. Oversee the development, deployment, and continuous improvement of AI/ML models and systems to ensure scalability, robustness, and real-time performance. Manage the full AI/ML lifecycle, including data strategy, model development, validation, deployment, monitoring, and retraining pipelines. Evaluate and incorporate cutting-edge AI technologies, frameworks, and external AI services to enhance capabilities and accelerate delivery. Establish and enforce engineering standards, best practices, and observability tools for model governance, performance tracking, and compliance with data privacy and security requirements. Collaborate with infrastructure and DevOps teams to design and maintain cloud infrastructure optimized for AI workloads, including GPU acceleration and MLOps automation. Manage project timelines, resource allocation, and cross-team coordination to ensure timely delivery of AI initiatives. Stay updated on emerging AI trends, research, and tools to continuously evolve the AI engineering function. Required Skills & Qualifications: 10 to 15 years of experience in AI, machine learning, or data engineering roles, with at least 8 years in leadership or managerial positions. Bachelors, Masters, or PhD degree in Computer Science, Statistics, Mathematics, or related fields from a top-tier college is preferred. Proven track record of leading AI engineering teams and delivering production-grade AI/ML systems at scale. Expertise in machine learning algorithms, deep learning, NLP, computer vision, and Generative AI technologies. Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Keras, Hugging Face Transformers, LangChain, MLflow, and related tools. Strong understanding of data engineering concepts, ETL pipelines, and distributed computing frameworks like Spark and Hadoop. Experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker). Familiarity with software engineering practices, CI/CD, version control (Git), and microservices architecture. Strong problem-solving skills with a product-oriented mindset and the ability to translate business requirements into technical solutions. Excellent communication skills for effective collaboration across technical and non-technical teams. Experience in AI governance, model monitoring, and compliance with data privacy/security standards. Preferred Qualifications: Experience in building or managing ML platforms or MLOps pipelines. Knowledge of NoSQL databases (MongoDB, Cassandra) and real-time data processing. Previous exposure to AI in domains like banking, finance, and credit is advantageous. This role presents an opportunity to lead AI innovation at scale, shaping the future of AI-powered products and services in a rapidly growing, technology-centric environment.,