Job Description:This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies.
- Lead design, development, and deployment of AI and non-AI applications across multiple business domains
- Own delivery accountability across planning, execution, testing, and production rollout
- Ensure alignment with enterprise architecture, security, and compliance standards
- Design and implement AI-powered solutions, including: Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, Agent-based architectures and orchestration frameworks
- Integrate AI capabilities into enterprise systems via APIs and microservices
- Evaluate and adopt emerging AI technologies (e.g., Copilot, Foundry, open-source frameworks)
- Develop scalable backend services using: Java (Spring Boot, Microservices architecture), Python (FastAPI, data pipelines, AI/ML frameworks)
- Build and optimize high-performance, resilient, and maintainable systems
- Ensure best practices in coding standards, testing, and code reviews
- Define solution architectures for complex systems involving: Distributed systems and microservices, Event-driven architectures, Cloud-native patterns (Azure/AWS/GCP)
- Ensure system observability (logging, monitoring, alerting)
- Drive production support readiness, including incident resolution and RCA
- Provide technical guidance to engineering teams
- Conduct design reviews and mentor junior/mid-level engineers
- Promote engineering best practices and continuous learning
- Partner with product owners, architects, and business stakeholders to translate requirements into technical solutions
- Communicate complex technical concepts to both technical and non-technical audiences
- Contribute to strategic initiatives and roadmap planning
Responsibilities:- Lead end-to-end delivery of AI and non-AI software solutions across multiple projects
- Design and implement scalable architectures (microservices, cloud-native, event-driven)
- Build and maintain backend systems using Java and Python
- Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms
- Translate business requirements into technical designs and high-quality implementations
- Ensure adherence to enterprise standards for security, compliance, and performance
- Drive code quality, testing, and engineering best practices across teams
- Implement and manage CI/CD pipelines, monitoring, and production readiness
- Provide technical leadership and mentorship to engineers and review solution designs
- Collaborate with stakeholders to align technology delivery with business goals
- Create and review technical design documents, architecture diagrams, and standards
- Drive reusability, modularity, and scalability across solutions
- Design and manage data ingestion, transformation, and processing pipelines
- Work with structured and unstructured data, including financial and operational datasets
- Implement feature engineering, model deployment, and monitoring pipelines
Required Qualifications:- 10+ years of experience in software engineering and system design
- Proven experience delivering large-scale enterprise applications and AI solutions
- Strong expertise in: Java (Spring Boot, Microservices), Python (AI/ML, APIs, data engineering)
- Hands-on experience with:
- AI/ML frameworks (OpenAI, Hugging Face, LangChain,etc.)
- RAG pipelines, embeddings, vector databases
- RESTful APIs, distributed systems
- Deep understanding of:
- Microservices, APIs, event-driven architectures
- Cloud platforms (Azure preferred)
- Containerization (Docker, Kubernetes)
- Practical experience with:
- LLM-based applications and prompt engineering
- Model lifecycle management (training, deployment, monitoring)
- AI governance, risk, and explainability (preferred in regulated industries)
- Strong problem-solving and analytical thinking
- Excellent communication and stakeholder management
- Ability to operate in a fast-paced, ambiguous environment
Desired Qualifications:- Experience in financial services or regulated industries
- Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)
- Familiarity with agent orchestration, MCP, and AI platform integration patterns
- Experience with data privacy, compliance, and secure AI deployments
Skills:- Automation
- Influence
- Result Orientation
- Stakeholder Management
- Technical Strategy Development
- Application Development
- Architecture
- Business Acumen
- Risk Management
- Solution Design
- Agile Practices
- Analytical Thinking
- Collaboration
- Data Management
- Solution Delivery Process
Shift:1st shift (United States of America)
Hours Per Week: 40