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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.  
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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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