SR GEN AI Engineer

Design, build, and deploy production-grade Generative AI solutions using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and enterprise integrations., * Build end-to-end GenAI applications using LLMs (text, documents, QA, summarization, extraction)

• Implement RAG pipelines, data ingestion, chunking, embeddings, vector search, and grounded responses
• Develop APIs and microservices to integrate GenAI solutions with enterprise systems
• Implement structured outputs, function calling, and prompt frameworks
• Support evaluation, testing, monitoring, and continuous improvement of GenAI solutions
• Collaborate closely with the GenAI Architect and Agentic AI Engineers, Build Agentic AI systems that go beyond chatbots - autonomous, goal-driven AI agents that can reason, plan, call tools, and execute multi-step workflows., * Design and implement agentic AI workflows (planning, execution, observation, iteration)
• Build tool-using AI agents integrated with APIs, data sources, and enterprise systems
• Implement multi-agent or orchestrated agent architectures where required
• Combine Agentic AI with RAG for grounded, enterprise-safe responses
• Implement guardrails, safety checks, and evaluation mechanisms
• Debug, optimize, and operationalize agentic workflows in production, Act as the technical anchor and AI coach for the program - defining the GenAI architecture, standards, and guardrails, while guiding developers and ensuring long-term scalability., * Define the overall GenAI reference architecture (LLM, RAG, Agentic AI, data, security)
• Establish standards for prompt design, RAG patterns, evaluation, testing, and deployment
• Guide and mentor GenAI and Agentic AI engineers on design and implementation choices
• Drive architecture and design reviews; ensure reusability across regions and teams
• Define governance, guardrails, and responsible AI practices
• Act as the bridge between business stakeholders and technical teams

• Hands-on experience with LLMs and RAG architectures
• Experience with vector databases and semantic search
• Practical understanding of prompt engineering and evaluation

2) Agentic AI Engineers (Autonomous AI Systems), * Hands-on experience building agentic or multi-step AI workflows (not just prompting)

• Experience with orchestration frameworks and tool calling
• Understanding of AI safety, guardrails, and reliability patterns
• Ability to operationalize AI agents at enterprise scale, * Strong hands-on background in GenAI and enterprise architecture
• Deep understanding of LLM, RAG, Agentic AI, and deployment patterns
• Experience coaching teams through AI adoption and transformation
• Ability to translate business goals into technical solutions, 1. Experience in designing, building, and maintaining the data infrastructure that powers machine learning systems - pipelines, feature stores, data lakes, and real-time streaming architectures that feed model training and inference at scale. 2. Led enterprise-wide architectural governance and helped create standards/frameworks driving efficiency. 3. Strong experience in Databricks, Delta Lake, Apache Spark, Python, and Apache Airflow.

Top 3 Preferred Skills

1. Any related certification
2. Worked in the life and annuities industry

Education: At least a bachelor’s degree (or equivalent experience) in Computer Science, Software/Electronics Engineering, Information Systems, or a closely related field is required for the project