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Tier-1 brand, metro hiring, and a popular Data Scientist title increase candidate competition despite GenAI specialization.
Role requires strong GenAI skills plus AML/compliance domain knowledge, moderately reducing cross-industry transferability.
Multiple mandatory GenAI, MLOps, cloud, and LLM skills plus explicit 2-3 years requirement increases filter strictness.
Design and implement generative AI models and applications specifically for Anti Money Laundering (AML) transaction monitoring use cases.
Build and optimize AI/ML pipelines including Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and LangChain/LangGraph for dynamic workflows and large language models.
Develop and deploy scalable AI solutions on cloud platforms (AWS/Azure), including evaluation frameworks and observability tools to monitor AI system performance, latency, and costs.
2-3 years hands-on experience in implementing and deploying large language models (LLMs) and generative AI solutions.
Proficiency in Python and AI/ML frameworks such as PyTorch or TensorFlow, including experience with RAG, MCP, tool calling, and LangChain/LangGraph.
Bachelor’s or Master’s degree in Computer Science or equivalent.
Experience with cloud platforms (AWS, Azure), data engineering tools like Databricks, and MLOps practices including CI/CD and model monitoring.
Experienced in developing AML transaction monitoring solutions using advanced generative AI and large language models.
Skilled in building end-to-end MLOps pipelines integrating GenAI models into production with focus on performance, security, and compliance.
Strong in applying evaluation methods for generative AI systems including hallucination detection, bias evaluation, and factuality scoring in a regulated environment.