Asks for

The ad says“Generative AI & Agentic Systems”
The ad says“• Experience working in lean, agile environments (e.g., SAFe or similar frameworks).”
The ad says“• Hands-on experience with at least one public cloud AI/GenAI platform (e.g., AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine).”
The ad says“• Hands-on experience with at least one public cloud AI/GenAI platform (e.g., AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine).”
The ad says“• Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.”
The ad says“• Hands-on experience with DevOps and CI/CD pipelines, infrastructure as code (e.g., Terraform), GitHub collaboration, and cloud deployments.”
The ad says“• Experience developing full-stack agentic solutions using agent frameworks such as ADK, A2A, MCP, LangChain, LangGraph, or CrewAI, and familiarity with commercial and open-source foundation models.”
The ad says“• Write high-quality, production-ready Python (e.g., asyncio, FastAPI, Pydantic) and instrument AI observability using OpenTelemetry, offline evaluation, and drift monitoring, while leveraging enterprise AI platforms and standards.”
The ad says“• Hands-on experience with DevOps and CI/CD pipelines, infrastructure as code (e.g., Terraform), GitHub collaboration, and cloud deployments.”
The ad says“• Hands-on experience with at least one public cloud AI/GenAI platform (e.g., AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine).”
The ad says“• Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.”
The ad says“• Experience developing full-stack agentic solutions using agent frameworks such as ADK, A2A, MCP, LangChain, LangGraph, or CrewAI, and familiarity with commercial and open-source foundation models.”
The ad says“• Experience developing full-stack agentic solutions using agent frameworks such as ADK, A2A, MCP, LangChain, LangGraph, or CrewAI, and familiarity with commercial and open-source foundation models.”
The ad says“• Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.”
The ad says“• Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.”
The ad says“• Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.”
The ad says“• Write high-quality, production-ready Python (e.g., asyncio, FastAPI, Pydantic) and instrument AI observability using OpenTelemetry, offline evaluation, and drift monitoring, while leveraging enterprise AI platforms and standards.”
The ad says“• Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.”
The ad says“• Write high-quality, production-ready Python (e.g., asyncio, FastAPI, Pydantic) and instrument AI observability using OpenTelemetry, offline evaluation, and drift monitoring, while leveraging enterprise AI platforms and standards.”
The ad says“• Strong Python development experience, building and operating production services and APIs.”
The ad says“• Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.”
The ad says“• Experience building and operating advanced RAG and Agentic RAG systems using modern techniques and methodologies.”
The ad says“• Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.”
The ad says“• Hands-on experience with DevOps and CI/CD pipelines, infrastructure as code (e.g., Terraform), GitHub collaboration, and cloud deployments.”

IND Lead Software Engineer - GCC093

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

Position Overview
We are seeking an AI/ML Engineer who will be responsible for architecting, building and deploying production-grade AI systems This is a highly hands-on role requiring deep expertise in ML engineering, MLOps, LLM architecture, and Generative/Agentic AI concepts and tooling exposure. This role is well-suited for someone who brings intellectual curiosity, a bias toward action, and a collaborative mindset, and who is looking to deepen their AI/ML engineering expertise while taking on increasing responsibility over time________________________________________
Key Responsibilities
•   Design and implement production-grade AI/ML and Agentic AI solutions that drive end-to-end transformation across pricing, underwriting, and sales.
•   Partner with Cloud, AIOps, Data Science, LOB IT, Enterprise Architecture, and Data teams to provision infrastructure, deploy services, and operate scalable AI platforms using modern DevOps practices.
•   Leverage AI Platform, agent development standards, and agent frameworks to build, deploy, monitor and maintain agentic solutions & AI/ML pipelines.
•   Architect and build highly available, scalable, secure, and fault-tolerant AI/ML systems, applying modern distributed system patterns such as event-driven, pub/sub, and point-to-point architectures.
•   Design and implement agent memory, evaluation, and feedback mechanisms to enable quality, safety, and reliability-driven tuning and continuous improvement.
•   Develop advanced context engineering, adaptive prompting, multi-agent coordination, and RAG/Agentic RAG systems using techniques such as HyDE, RAPTOR, and GraphRAG to improve accuracy and relevance.
•   Write high-quality, production-ready Python (e.g., asyncio, FastAPI, Pydantic) and instrument AI observability using OpenTelemetry, offline evaluation, and drift monitoring, while leveraging enterprise AI platforms and standards.
________________________________________

Required Skills & Experience:
Experience Range - 4 to 6 Years
•   Bachelor’s or Master’s degree in computer science , Software Engineering, Data Science, or a closely related discipline.
•   Professional experience in ML, Software Engineering, or a related role, including 3+ years delivering AI/ML solutions in production.
•   Strong Python development experience, building and operating production services and APIs.
Generative AI & Agentic Systems
•   Experience developing full-stack agentic solutions using agent frameworks such as ADK, A2A, MCP, LangChain, LangGraph, or CrewAI, and familiarity with commercial and open-source foundation models.
•   Experience building and operating advanced RAG and Agentic RAG systems using modern techniques and methodologies.
•   Experience with agentic monitoring, observability, and model evaluation frameworks to assess quality, safety, and performance in production.
ML, Platforms & Cloud
•   Hands-on experience with ML and AI frameworks such as PyTorch, Hugging Face, Pandas, NumPy, and related libraries.
•   Hands-on experience with at least one public cloud AI/GenAI platform (e.g., AWS SageMaker/Bedrock or Google Vertex AI, Vertex AI Search, and RAG Engine).
Software Engineering, DevOps & Security
•   Experience designing and delivering production-grade APIs and microservices using modern software engineering practices.
•   Hands-on experience with DevOps and CI/CD pipelines, infrastructure as code (e.g., Terraform), GitHub collaboration, and cloud deployments.
•   Experience with DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.
Ways of Working & Communication
•   Experience working in lean, agile environments (e.g., SAFe or similar frameworks).
•   Strong communication and collaboration skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders, influence decisions, and work effectively across teams.
________________________________________Nice to Have
•   Knowledge of automated testing, validation gates, canary deployments, and rollback strategies for ML and Agentic AI systems.
•   Experience designing and implementing data pipelines for ML and Agentic AI workloads using modern data platforms (e.g., Snowflake, Airflow, S3/Glue/EMR/Redshift, Apache Iceberg, or equivalent).
•   Experience working in insurance or other regulatory environments.
•   Ability to partner with governance, risk, compliance, and security teams to ensure responsible AI through techniques such as bias mitigation, disparate impact analysis, and counterfactual testing.

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