Senior Software Engineer/AI-ML

About the Role

As a Senior Software Engineer specializing in AI and ML, you will design and implement innovative AI solutions that transform various operational processes. You will collaborate with cross-functional teams to develop scalable systems and ensure the seamless integration of AI technologies into existing workflows.

Key Responsibilities

• Responsible for design and implementation of AI/ML solutions, enabling comprehensive end-to-end transformation and process reimagination in underwriting, claims, operations, and corporate functions.
• Collaborate with Data Science Practitioners, LOB IT leads, EA, Data, and AI architects to develop solutions and integrate into operational processes and systems supporting various functions.
• Design, build and maintain scalable Agentic AI systems, including multi-agent workflows, remote Agent orchestration, tool calling, and human-in-the-loop (HITL) feedback.
• Implement Evaluation-driven development harness, grading logic, rubrics for evaluating AI Agents and tuning it for quality, safety, and reliability.
• Design and implement AI Agent memory systems to support hyper personalized multi-turn conversation, and self-improvement from HITL feedback (Episodic memory).
• Build full stack AI Agents with latest Agentic AI/UI frameworks & standards.
• Leverage AI Platform and agent and model operations frameworks to automate and streamline build, deployment, monitoring and maintenance of agent solutions, AI/ML pipeline, machine learning and data science models.
• Contribute to our starter packs, Horizontal Agents, and SDKs to tailor and deploy solutions across various use cases accelerating time to market.
• Apply advanced context engineering techniques to build complex multi-agent systems.
• Design and implement adaptive/dynamic prompting using various techniques.
• Collaborate with AIOps, Platform, and Cloud teams to set up infrastructure and deploy Cloud services and tools.
• Develop advanced RAG systems and use advanced techniques & methodology to enhance accuracy and relevancy.
• Build production grade ML/DL models using PyTorch, TensorFlow, scikit learn.
• Develop and deploy backend inference services for machine learning models.
• Write high-quality Python code using advanced libraries that complies with coding standards.
• Collaborate closely with MLOps, Cloud and infrastructure teams to ensure seamless deployment, operation, and maintenance of AIML systems.
• Instrument AI observability using OpenTelemetry tooling.
• Build robust ETL/ELT pipelines using Python and PySpark for training ML Models and AI Agents.
• Apply AIML system architecture and design patterns by selecting the blueprint that best fits use case needs.
• Build scalable, fault-tolerant solutions on AWS and/or GCP in a multi-cloud ecosystem.