Experience:

• 4 to 6 Years
• 3+ years building production services using FastAPI, Asyncio, and Pydantic
• Professional experience in Machine Learning, Software Engineering, or related field
• Experience designing and delivering AI/ML solutions in production environments
• Experience working in lean, agile environments using Scaled Agile Framework (SAFe) or similar methodologies
• Experience mentoring and developing junior AI engineers or data engineers

Qualification:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or related discipline

Responsibilities:

• Design and implement AI/ML solutions for underwriting, claims, operations, and corporate functions
• Collaborate with Data Science Practitioners, LOB IT leads, EA, Data, and AI architects
• Design, build, and maintain scalable Agentic AI systems and multi-agent workflows
• Implement evaluation-driven development harness and grading logic for AI Agents
• Design AI Agent memory systems for personalized multi-turn conversations
• Build full stack AI Agents using latest Agentic AI/UI frameworks
• Leverage AI Platform and model operations frameworks to automate build, deployment, monitoring, and maintenance
• Contribute to starter packs, Horizontal Agents, and SDKs for solution deployment
• Apply advanced context engineering techniques for multi-agent systems
• Design and implement adaptive/dynamic prompting techniques
• Collaborate with AIOps, Platform, and Cloud teams for infrastructure setup and deployment
• Develop advanced RAG systems using advanced techniques
• Build production-grade ML/DL models for various use cases
• Develop and deploy backend inference services for ML models
• Write high-quality Python code adhering to coding standards
• Collaborate with MLOps, Cloud, and infrastructure teams for deployment and maintenance
• Instrument AI observability and set up offline evaluation and drift monitoring
• Build robust ETL/ELT pipelines for training ML models and AI Agents
• Apply AI system architecture and design patterns
• Build scalable, fault-tolerant solutions on AWS and/or GCP
• Apply modern distributed system design patterns

Additional responsibilities:

• Troubleshoot platform issues with AIOps engineer
• Communicate complex technical concepts to technical and non-technical audiences
• Influence leadership decisions
• Collaborate across teams to make informed technical decisions and resolve conflicts
• Provide AI thought leadership and align technical deliverables with enterprise strategies
• Plan, organize, and execute work effectively in fast-paced environments
• Show innovation, continuous learning, ownership, accountability, and urgency in delivering business outcomes

Nice to have:

• Experience with PySpark, Rust, NodeJS, and/or Typescript
• Experience with Infrastructure as Code (Terraform), Cloud Build, and/or Cloud Formation
• Knowledge of automated testing, validation gates, canary deployments, and rollback strategies for ML and Agentic AI systems

More skills:

AI/ML solutions design and implementation, Agentic AI systems, Multi-agent workflows, Human-in-the-loop (HITL) feedback, Evaluation-driven development, AI Agent memory systems, Agentic AI/UI frameworks (A2A, AAIF, A2UI, Agent skills, MCP), AI Platform and model operations frameworks (AgentOps, AIOps, FMOps), Context engineering techniques, Adaptive/dynamic prompting, Vertex AI SDK, Cloud services deployment (AWS, GCP), DevOps tools and release management, RAG systems (Agentic RAG, HyDE, RAPTOR, GraphRAG), ML/DL models (PyTorch, TensorFlow, scikit-learn), Backend inference services (FastAPI/REST), Python programming (asyncio, FastAPI, Pydantic), AI observability (OpenTelemetry), ETL/ELT pipelines (Python, PySpark), AI system architecture and design patterns, Distributed system design patterns (sagas, CQRS, event-driven architectures), Generative AI and Agentic AI solutions, Semantic search and embedding models, Foundation models (BERT, GPT), Single- and multi-agent frameworks (LangChain, LangGraph, CrewAI), Cloud-based GenAI and AI platforms (AWS SageMaker, Bedrock, Google Vertex AI), Jupyter environments, AutoML workflows, experimentation tracking, Production-grade APIs and microservices, DevOps and CI/CD pipelines (Jenkins, Terraform, GitHub), Software engineering best practices (SOLID, 12-Factor App, IoC, sagas), Identity and access management (OAuth 2.1, OpenID Connect), ML and AI libraries (Keras, Hugging Face, NumPy, Pandas), Traditional machine learning techniques (feature engineering, EDA, hyperparameter tuning, XGBoost, GLMs, KNN, PCA, SVM), DevSecOps tools (Nexus, SonarQube, Checkmarx, mcp-scan)