AI Engineering Lead

This role provides expert-level system analysis, design, development, and implementation of applications and databases, and directs business assessment and requirements analysis for hardware and operating systems, while architecting complex data integration and driving system performance and scalability improvements.

Responsibilities
1. Provides expert level system analysis, design, development, and implementation of applications and databases. Integrates third party products. Ensures that expected application performance levels are achieved.
2. Ensures the viability of IT deliverables. Recommends development options, including design, build/buy, and vendor purchase. Conducts testing, including functionality, technical limitations, and security.
3. Elevates code into the development, test, and production environments on schedule. Provides follow up production support. Submits change control requests and documents.
4. Provides subject matter expertise in software development methodology and development architecture standards. Mentors and trains staff with less experience. Resolves elevated issues and recommends enterprise-wide improvements.
5. Participates in design, code, and test inspections throughout the life cycle to identify issues. Explains technical considerations at related meetings, including those with internal clients. Performs systems analysis activities.
6. Thoroughly understands client business functions and technology needs. Has a broad and deep understanding of Vanguard's technologies, tools, and applications, including those that interface with business area and systems. Is well versed on the latest technologies and tools supporting software development in the industry.
7. Interfaces with cross functional team members and communicates systems issues at the appropriate technical level for each audience.
8. Thoroughly understands and complies with Information Technology and Information Security policies and procedures and verifies that deliverables meet requirements.
9. Participates in special projects and performs other duties as assigned.
10. Architect and guide integration of complex data flows across distributed services, APIs, and platforms to enable reliable, secure, and seamless enterprise-level interoperability.
11. Drive performance and scalability enhancements by proactively analyzing system behavior, optimizing database architecture, and implementing high-efficiency design patterns.

Qualifications and Skills

• Minimum 8 years of experience in application development, system architecture and database management.
• Bachelor's degree (B.E./B.Tech) or master's degree (M.E./MTech) in relevant fields such as Computer Science, Information Technology, Engineering, or a related field.

• Strong programming skills in Python (primary), with experience in Java / TypeScript a plus Solid understanding of data structures, algorithms, and software engineering fundamentals

• Experience building production-grade APIs and services (REST, async, scalable patterns) Familiarity with Git, CI/CD pipelines, and Agile delivery

• Machine Learning & GenAI, Hands-on experience with LLMs (e.g., prompt engineering, embeddings, summarization, Q&A)

  Experience with ML frameworks such as PyTorch, TensorFlow, or equivalent

  Understanding of model evaluation, performance metrics, and error analysis

  Experience fine-tuning, orchestrating, or integrating foundation models (vendor or open source)

• Retrieval, Knowledge & Tooling

  RAG (Retrieval-Augmented Generation) design and implementation

  Vector databases / search (e.g., embeddings, similarity search, metadata filtering)

  Knowledge ingestion pipelines (documents, structured + unstructured data)

  Tool/function calling and agent-style workflows

• Cloud & Platform Engineering

  Cloud-native development on AWS / Azure / GCP (Vanguard-aligned experience preferred)

  Containers and orchestration (Docker, Kubernetes)

  Event-driven and distributed systems

  Observability: logging, metrics, tracing, model monitoring

• Responsible AI & Governance (Critical)

  Understanding of Responsible AI principles (privacy, bias, explainability, safety)

  Experience working with enterprise controls: security reviews, data protection, compliance

  Familiarity with model risk management, auditability, and guardrails

  Ability to operationalize AI safely in regulated environments

  • Product & Execution Mindset

    Strong ability to translate ambiguous business problems into technical solutions

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