AI VP/SVP Engineering

Responsibilities Own engineering delivery for GenAI application initiatives: from problem definition to production rollout and iteration. Lead development of agentic applications (multi-step reasoning/workflows) with robust orchestration, safety, evaluation, and monitoring. Build and scale RAG systems (retrieval, ranking, context construction, grounding, citations, hallucination control) for text-heavy domains. Drive best practices for prompt engineering, tool-use patterns, function calling, guardrails, and quality loops. Partner with Product/Business stakeholders to define success metrics, SLAs, and measurable outcomes (quality, latency, cost, conversion). Establish engineering excellence around scalable backend systems: APIs, workflow engines, async systems, reliability, observability, and cost controls. Hire, mentor, and manage a team of engineers and ML practitioners; build a culture of ownership, speed, and quality. Collaborate with platform/data/search teams for feature stores, data pipelines, indexing, retrieval, experimentation, and evaluation frameworks. Qualifications Strong experience in building ML-powered production applications (end-to-end). Strong hands-on experience with Python and modern ML development practices. Proven expertise in NLP / text data (classification, extraction, embeddings, semantic matching, etc.). Experience building or leading teams delivering Generative AI applications and/or agentic systems. 4+ years of people management experience (hiring, mentoring, performance management). Practical experience with: o Machine Learning, Deep Learning, Generative AI o Prompt engineering, RAG, tool use/function calling o Scalable backend systems (APIs, services, distributed systems, async patterns) o SQL databases (any) and NoSQL (at least one) Good-to-Have Skills Experience with search datastores like Elasticsearch / Solr / Vespa Background in Information Retrieval (ranking, relevance, query understanding) Experience with Vector Search and hybrid retrieval (BM25 + embeddings) Experience managing remote / distributed teams (multi-location, async collaboration, outcomes-based execution) Preferred Traits (What We Value) Strong product sense: you can translate ambiguous problems into shippable milestones. Engineering rigor: you care about reliability, observability, evaluation, and operational excellence. Comfortable operating in fast-moving environments with high ownership and accountability. Ability to communicate clearly with stakeholders across engineering, product, and leadership. Why Info Edge / Naukri.com Work on large-scale, high-impact systems shaping the future of hiring. Opportunity to lead applied GenAI innovation with meaningful data, reach, and business outcomes. Collaborate with strong teams across platform, data, ML, and product.