The Role You will own the Al/ML architecture of the Eigenwave platform(An incubator company of skeps), from data foundations and model development to production serving, monitoring and governance. You will design the intelligent systems that sit inside a multi-lender financing platform, such as credit decisioning, fraud detection, lender-offer matching and personalisation, and make sure they are accurate, explainable and safe to run on real money. This is a hands-on architecture role. You have taken machine learning products from experiment to production at scale, and you stay close to the code: reviewing designs, prototyping, and unblocking data scientists and engineers on the critical path. You will also guide how Al is adopted across the product and engineering organisation.

What You'll Do
Define the Al/ML architecture. Own the end-to-end design of data ingestion, feature engineering, model training, serving and monitoring. Make sure the platform is scalable, maintainable, secure and able to evolve without rewrites. Build ML for lending and payments. Architect solutions for credit risk scoring, fraud and anomaly detection, loan eligibility and offer matching across multiple lenders, and personalisation for merchants and customers. Design LLM and GenAl capabilities. Lead RAG pipelines, Al agents, document intelligence and conversational assistants, with strong guardrails for accuracy, privacy and responsible use. Own MLOps and model lifecycle. Set up CI/CD.for models, experiment tracking, a model registry, automated retraining, drift detection and observability, so models stay reliable after release. Integrate with the core platform. Expose Al/ML services to the Node.js and Python microservices through well-designed REST APIs and event-driven pipelines, with idempotency and graceful failure handling for transactional workloads.





What You'll Do
Define the Al/ML architecture. Own the end-to-end design of data ingestion, feature engineering, model training, serving and monitoring. Make sure the platform is scalable, maintainable, secure and able to evolve without rewrites. Build ML for lending and payments. Architect solutions for credit risk scoring, fraud and anomaly detection, loan eligibility and offer matching across multiple lenders, and personalisation for merchants and customers. I
• Design LLM and GenAl capabilities. Lead RAG pipelines, Al agents, document intelligence and conversational assistants, with robust guardrails for accuracy, privacy and responsible use. Own MIOps and model lifecycle. Set up CI/CD for models, experiment tracking, a model registry, automated retraining, drift detection and observability, so models stay reliable after release. Integrate with the core platform. Expose Al/ML services to the Node.js and Python microservices through well-designed REST APls and event-driven pipelines, with idempotency and graceful failure handling for transactional workloads. Own security, risk and compliance. Be accountable for data protection, access controls auditability, explainability and fairness of models that make financial decisions. Partner with legal and compliance on regulatory expectations for digital lending and certifications such as ISO 27001, SOC 2 and PCI-DSS Partner across teams. Work with engineering managers,



tech leads, data scientists, product managers, UX designers and business stakeholders to define priorities and tie Al work to measurable business outcomes. What You Bring 10-12 years of software engineering experience, including 4+ years designing and deploying AI/ML solutions in production, with a strong focus on architecture and design. Built products from scratch. You have taken end-to-end Al-enabled products to production in a startup or product organisation and lived with the consequences. Strong ML foundations. Python with scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM. Pandas and NumPy. Solid grounding in model evaluation, bias and fairness, and explainability. LLMs and GenAl. Prompt engineering, fine-tuning, RAG, embeddings and vector databases, with frameworks such as LangChain or Llamalndex. MLOps. Experience with Mlflow, Kubeflow, Airflow, SageMaker or equivalent, including model versioning, monitoring and automated pipelines. Data and distributed systems. Data pipelines at scale using SQL and NoSQL stores, Spark and Kafka or similar, event-driven design, and idempotency and failure handling for transactional systems. Server-side and API skills. Node.js and Python services, RESTful APls in Express or EastAPl, microservice design, and the ability to review and challenge an architecture. Cloud and DevOps. AWS (preferred), Azure or GP, including managed Al/ML services, plus Docker, Kubernetes, Git workflows and CI/CD. Security and privacy mindset, with experience protecting sensitive data and owning risk trade-offs in a regulated or data-sensitive domain. Collaboration and mentoring. You work well with product managers, data scientists and UX designers, and you grow the people around you. •

📌 AI/ML Architect (Gurugram)
🏢 Skeps
📍 Gurugram