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.