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Tier-1 employer, mid-level experience, metro location, and broad ML/big-data skillset increase competition.
Core ML, big-data, and MLOps skills are broadly transferable across industries, lowering background sensitivity.
Explicit 5+ years plus extensive mandatory ML, big-data, cloud, Java/Scala, and MLOps requirements.
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Design, develop, and deploy scalable AI/ML solutions and large language model (LLM) applications using techniques such as prompt engineering, RAG, and fine-tuning.
Build and maintain scalable data architectures, pipelines, and big data environments across on-premises and cloud platforms for rapid prototyping and production.
Develop agentic AI systems with multi-agent workflows and autonomous decision loops, collaborating with cross-functional teams to translate business requirements into technical solutions.
5+ years of relevant experience in data science, AI, or machine learning roles.
Bachelor’s degree in computer science or related field; Master’s preferred but not mandatory.
Strong programming skills in Python, Java, and/or Scala, with experience in big data technologies like Hadoop and Spark.
Experience working with cloud platforms (AWS, Azure, or Google Cloud), big data environments, and tools such as Kafka, Flume, and deployment CI/CD pipelines.
Experienced in building production-grade AI/ML systems including LLM-powered applications and multi-agent autonomous systems.
Skilled in end-to-end AI/ML lifecycle including designing prototypes, scalable data architecture, and model deployment with MLOps frameworks.
Comfortable operating at the intersection of research and engineering, contributing to innovation and proof-of-concept projects, preferably with exposure to fintech or financial services domains.