Build and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services.
Apply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents.
Implement robust testing (unit/integration), performance benchmarking (latency/cost), and observability (logging/metrics/tracing) for AI services.
Collaborate with cross-functional stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics clearly to technical and non-technical audiences.
Strong problem-solving skills and ability to work effectively in ambiguous environments with multiple stakeholders.
Required Qualifications, Capabilities, and Skills
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 (e.g., RAG, classification, extraction, ranking, summarization, copilots).
Familiarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces.
Familiarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation.
Strong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code.
Proficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems.
Experience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence.
Experience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation.
Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and production monitoring practices.
Preferred Qualifications, Capabilities, and Skills
Experience building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies).
Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference.
Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
Experience fine-tuning models (e.g., LoRA/QLoRA/DoRA) and/or working with SLMs, embeddings, and retrieval systems.
Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
Exposure to distributed compute/training concepts (e.g., DDP, sharding) and performance/cost optimization.
