Senior Business AI Engineer, VP

Job description

**Position Overview** **Job Title: Senior Business** **AI Engineer** **Corporate Title: Vice President** **Location: Pune, India** **Role Description** - We are looking for a senior professional who can lead the safe and effective use of AI in business-critical systems. In this role, you will define how AI is applied to business workflows, ensuring outputs are reliable, explainable, and aligned with business rules. - You will build and enforce guardrails, define validation patterns, and design orchestration layers where AI and business logic work together seamlessly. - You will also establish frameworks to detect and manage issues like model drift and prompt drift, ensuring AI systems remain trustworthy and production-ready over time. This is a leadership role focused on making AI a reliable, explainable, and accountable component of business-critical Credit Risk platforms. - You are expected to work closely with business stakeholders, risk teams, and engineering groups to deliver practical AI solutions that are aligned with regulatory, operational, and domain-specific requirements. **What We’ll Offer You** As part of our flexible scheme, here are just some of the benefits that you’ll enjoy, - Best in class leave policy. - Gender neutral parental leaves - 100% reimbursement under childcare assistance benefit (gender neutral) - Sponsorship for Industry relevant certifications and education - Employee Assistance Program for you and your family members - Comprehensive Hospitalization Insurance for you and your dependents - Accident and Term life Insurance - Complementary Health screening for 35 yrs. and above **Your Key Responsibilities** - Define AI Application Strategy: Determine how and where AI should be applied across business workflows in the Counterparty Dashboard, Security Service, and CPA/CDA platforms. - Build Guardrails & Validation Patterns: Design and enforce safety guardrails, validation layers, and policy checks to ensure AI outputs are reliable, explainable, and aligned with business rules. - Orchestration Layer Design: Architect orchestration layers where AI components and deterministic business logic work together, ensuring traceability and auditability of decisions. - Drift Detection & Management: Establish frameworks and monitoring strategies to detect and remediate model drift, prompt drift, and data drift in production environments. - Reliability & Explainability: Ensure AI-driven outputs are interpretable, auditable, and aligned with risk and compliance requirements typical of financial services. - Stakeholder Collaboration: Partner with business stakeholders, risk officers, product owners, and engineering teams to translate business needs into safe, production-grade AI solutions. - Engineering Leadership: Provide technical leadership to Java engineering teams building services for counterparty risk, security, and client/product data platforms. - Production Readiness: Drive standards for testing, deployment, observability, and incident response for AI-augmented services. - Mentorship: Mentor senior and mid-level engineers on AI integration patterns, secure design, and best practices for trustworthy AI in regulated environments. **Your Skills And Experience** **Core Engineering** - 9–12 years of hands-on experience in software engineering, with deep expertise in Java and the Spring / Spring Boot ecosystem. - Strong experience designing and building distributed, high-throughput, low-latency backend services. - Proven experience with microservices architecture, REST APIs, event-driven systems (Kafka or similar), and relational/NoSQL databases. - Experience with CI/CD pipelines, containerization (Docker/Kubernetes), and cloud platforms (AWS / Azure / GCP). **AI & ML Integration** - Practical knowledge of AI/LLM concepts: prompts, embeddings, retrieval-augmented generation (RAG), and model orchestration. - Experience designing guardrails, validation patterns, and policy enforcement for AI outputs. - Understanding of orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or equivalent custom orchestration). - Familiarity with model/prompt drift detection, evaluation pipelines, and observability for AI systems. - Awareness of responsible AI principles — explainability, fairness, traceability, and auditability. **Business Domain Knowledge** - Strong understanding of financial services domains, ideally counterparty risk, security services, CPA/CDA, or similar workflow-driven systems. - Familiarity with regulatory, compliance, and audit requirements that influence AI use in financial systems. **Leadership & Collaboration** - Demonstrated ability to lead architectural discussions and influence cross-functional teams. - Strong communication skills to engage both business stakeholders and engineering teams. - Experience mentoring engineers and shaping engineering standards. **Engineering Mindset** - Trust-First Engineering: Treat reliability, explainability, and safety as first-class engineering requirements — not afterthoughts. - Pragmatic Innovation: Apply AI where it adds clear, measurable business value; avoid AI for its own sake. - Production-Grade Discipline: Strong bias toward observability, testing, and operational readiness for every AI-enabled component. - Business Alignment: Continuously align technical decisions with business rules, risk posture, and domain constraints. - Continuous Learning: Stay current with evolving AI capabilities, frameworks, and best practices, and bring proven ideas back into the team. - Ownership & Accountability: Take end-to-end responsibility for the AI-augmented systems you build — from design to drift management in production. - Collaboration Over Silos: Work transparently across business, risk, and engineering to deliver solutions that are practical, safe, and trustworthy. **How We’ll Support You** - Training and development to help you excel in your career. - Coaching and support from experts in you