Applied AI Engineer
AmbakHaryāna, Republic Of India, IN12 hours ago
Job descriptionAI / ML Development : Architect and implement advanced ML systems for fraud detection, credit scoring, personalization, and risk modeling. Generative AI Solutions : Build and deploy LLM-powered tools for financial document summarization, chatbot automation, KYC workflows, and customer support. LLM Experimentation : Work with OpenAI, Claude, LLaMA, Mistral, etc.—covering prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG). MLOps & Deployment : Own CI / CD for models, scalable serving, monitoring, and explainability (using MLflow, Kubeflow, or GCP / AWS / Azure ML stacks). Cross-Functional Collaboration : Partner with product, compliance, and engineering teams to embed AI responsibly into fintech journeys. Model Governance : Ensure fairness, bias mitigation, explainability (XAI), and adherence to financial regulatory standards. Team Leadership : Mentor junior engineers and data scientists, fostering a high-performance AI culture. Prior experience with fintech AI use-cases (fraud, risk, credit scoring, personalization). Exposure to LangChain, Hugging Face, OpenAI APIs. Experience with MLOps stacks (MLflow, SageMaker, Vertex AI, Kubeflow). Contributions to AI research, open-source projects, or technical blogs / papers. Delivered at least 2 production-grade AI / GenAI systems impacting customer experience or financial operations. Established scalable, compliant, and monitored ML pipelines. Built a strong AI / ML engineering foundation for Ambak.Com’s long-term roadmap. Actively mentored team members and elevated AI culture across the org.
Why This Role is Special
- Greenfield + Impact : Build GenAI-powered financial solutions from scratch with minimal legacy baggage.
- Enterprise-grade Scale : Your models will power risk, fraud, and personalization systems used in high-stakes fintech environments.
- Innovation at Core : From RAG pipelines to fine-tuned LLMs, you’ll explore and deploy the latest AI techniques.
- End-to-End Ownership : From idea to production—data pipelines, modeling, compliance, and observability—you’ll drive the full cycle.
Key Responsibilities :
Preferred Qualifications
What Success Looks Like in 6 Months
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Applied Ai Engineer • Haryāna, Republic Of India, IN
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