Description:
Lead large-scale engineering initiatives, mentor senior engineering talent, and actively contribute to solution design and development. Drive Agentic AI development across product lines, leveraging LLMs for implementing business use cases and AI-driven automation to accelerate SDLC and deliver business value. Architect scalable, high-performing systems using cloud-native architecture and proven design patterns. Build foundational frameworks across middleware and UI to accelerate development and reduce maintenance overhead. Lead data engineering efforts, including: Data modeling, Data pipelines (ETL/ELT), Batch and streaming data processing, Data governance (catalog/lineage) and quality engines, Big data technologies. Align engineering initiatives with SAFe Agile principles, ensuring predictable delivery and continuous improvement.
Responsibilities:
Actively contribute to product requirements analysis and solution architecture. Lead proof-of-concepts, design of critical components, and code reviews. Implement agents using Google's ADK. Implement foundational components across Java, Python, and React or Angular for enterprise-grade applications. Champion AI-based code generation tools to automate repetitive engineering tasks. Collaborate with DevOps teams to optimize CI/CD pipelines, containerization (Docker/Kubernetes), and observability. Mentor and lead a global team of engineering leads and senior developers. Foster a culture of innovation, technical excellence, and accountability. Ensure compliance with security, regulatory, and performance standards.
Qualifications:
15%2B years in software engineering, with 8%2B years in hands-on leadership roles. Strong expertise in microservices, RESTful APIs, and distributed architectures. Experience in Python, FastAPI, Java, Spring AI, Async Programming, Gemini, OpenAI, Claude, Llama, Local Models, PyTorch, TensorFlow, Fine-tuning, Google's ADK, LangChain, LlamaIndex, Hugging Face, LangGraph, Multi-Agent Systems, PostgreSQL, Vector DBs, Advanced Retrieval, Docker, Production APIs, Monitoring, Prompt Engineering, Workflow Design, GenAI Optimization. Bachelor's or master's degree in engineering or sciences preferred. Experience with Financial Risk and Data. Certifications in AI/ML engineering & Cloud Architecture (AWS/Azure/Google Cloud Platform).
Hiring Terms:
No travel required
Clearance: Other
Additional Details:
Experience with Financial Risk and Data. Certifications in AI/ML engineering & Cloud Architecture (AWS/Azure/Google Cloud Platform).