Applied AI ML Lead – Python & Agentic AI

Overview

In this role you design, build, and productionize GenAI and Agentic AI solutions that enhance automation and decision-making across business workflows. You will work with cross-functional teams to deliver LLM/SLM-powered apps, RAG systems, and tool-using agents, while ensuring secure, scalable production services. You shape prompt strategies, data pipelines, and MLOps practices to improve quality and reliability. This role offers impact at scale within a leading financial-services firm that values innovation, security, and collaborative delivery.

Responsibilities

• Design, develop, and deploy GenAI and Agentic AI solutions for automation and user experience improvements in business workflows
• Build LLM/SLM-powered apps including RAG systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents
• Develop production-grade services (Java/Python) with REST/gRPC APIs and microservices while following secure coding and reliability practices
• Create prompt strategies and automated evaluation to raise quality over time
• Build and maintain data pipelines for ML/GenAI use cases using cloud services
• Apply MLOps across experimentation, versioning, CI/CD, deployment, monitoring, and maintenance
• Implement robust testing (unit/integration), performance benchmarking, and observability for AI services
• Collaborate with stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics to technical and non-technical audiences
• Demonstrate strong problem-solving in ambiguous environments with multiple stakeholders

Key requirements

• Undergrad or Master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field
• Hands-on experience building applied AI/ML or GenAI solutions (RAG, classification, extraction, ranking, summarization, copilots)
• Familiarity with MCP (Model Context Protocol), Agent Skills, and architectures connecting models to tools/data
• Experience with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation
• Strong software engineering experience delivering production systems and clean, testable code
• Proficiency in Java and/or Python; building APIs/services and integrating with data sources/downstream systems
• Experience deploying on AWS and cloud-native environments; knowledge of security fundamentals and operational excellence
• Experience with CI/CD, code reviews, unit testing (pytest/JUnit), and deployment automation
• Experience with containers and orchestration (Docker, Kubernetes/EKS) and production monitoring

• strong problem-solving
• effective communication
• collaboration across cross-functional teams
• Java and/or Python
• REST/gRPC APIs and microservices
• MLOps (experimentation, versioning, deployment, monitoring)

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