Lead Data & AI Engineer
Qualys, Inc.2 hours ago
Pune, IndiaSenior / Staff+
Responsibilities
- Define and help execute the enterprise AI strategy and roadmap across applications, data platforms, and workflows.
- Design, develop, deploy, and scale AI/GenAI applications, RAG solutions, intelligent agents, and workflow automation.
- Build AI capabilities on Data Lake, Lakehouse, cloud data, structured, unstructured, and real-time data platforms.
- Architect backend services, APIs, integrations, and orchestration frameworks using LLMs, model APIs, enterprise systems, and governed data.
- Define AI governance, evaluation, observability, performance, security, and reliability frameworks.
- Collaborate with business and enterprise application teams to identify and deliver high-impact AI use cases.
- Prototype and scale secure, production-ready AI solutions while optimizing performance, scalability, and cost.
- Develop reusable AI frameworks, architecture patterns, accelerators, and best practices for enterprise AI adoption.
Requirements
- 7–12+ years of experience in software engineering, AI/ML, or GenAI application development.
- Experience designing and implementing AI/ML or GenAI solutions on cloud-based data platforms such as Snowflake, Databricks, OCI, Data Lakes, Lakehouses, or enterprise analytics ecosystems.
- Strong knowledge of enterprise data architecture, data lakes, Lakehouses, semantic layers, data pipelines, governance, and AI-ready data foundations.
- Strong expertise in Python, backend development, APIs, system integration, and workflow orchestration.
- Hands-on experience with RAG, LLMs, prompt engineering, tool and agent calling, AI evaluation, and observability.
- Experience integrating enterprise applications, data platforms, and services through APIs and event-driven architectures.
- Strong analytical, systems-thinking, and problem-solving skills, including the ability to translate ambiguous business problems into scalable AI solutions.
- Preferred experience with vector databases, embeddings, semantic search, advanced RAG architectures, data governance, data quality, lineage, security, compliance, SQL, data engineering, automation platforms, AI governance frameworks, and reusable AI platforms.
- BE/B.Tech or MCA, preferably in Computer Science, Information Technology, or a related field.
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