Job Title

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. Someone with a diverse background/experience and an engineer at heart will fit into this role easily. 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. Such as A2A, AAIF, A2UI, Agent skills, and MCP. Leverage AI Platform and agent and model operations frameworks (e.g., AgentOps, AIOps, FMOps) to automate and streamline build, deployment, monitoring and maintainance of agentc solutions, AI/ML pipeline, machine learning and data science models. Contribute to our starter packs (ADK/MCP), Horizontal Agents, and SDKs to tailor and deploy solutions across various use cases accelerating time to market. Apply advanced context engineering techniques like context splitting, advanced coordination, UX negotiation, checkpointing, and context offloading to build complex multi-agent systems using A2A and Agent fabric. Design and implement adaptive/dynamic prompting using various techniques like automated prompt optimizer, DSPy etc. Hands-on expertise with prompt management libraries using Vertex AI SDK is a plus. Collaborate with AIOps, Platform, and Cloud teams to set up infrastructure and deploy Cloud services and tools on the HIG AI platform, while integrating DevOps tools and release management workflow. Troubleshoot platform issue along with AIOps engineer. Develop advanced RAG systems, such as Agentic RAG, and use advanced techniques & methodology like HyDE, RAPTOR, and GraphRAG to enhance accuracy and relevancy. Build production grade ML/DL models using PyTorch, TensorFlow, scikit learn for anomaly detection, segmentation, risk scoring, recommendation system, rating & pricing models. Develop and deploy backend inference services for machine learning models using FastAPI/REST to the LOB-serving MLOps platform. Write high-quality Python code using advanced libraries such as asyncio, FastAPI, and Pydantic that complies with our HIG coding standards and passes all quality checks. Collaborate closely with MLOps, Cloud and infrastructure teams to ensure seamless deployment, operation, and maintenance of AIML systems. Instrument AI observability using OpenTelemetry (OTel) tooling. Set up offline evaluation (LLM-as-a-judge, RAGAS scoring, ROUGE/BLEU where applicable), drift monitoring and playbacks in our Observability platform. 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. Contribute to AI Architecture by suggesting new patterns, identifying innovative approaches, and improving existing ones. Build scalable, fault-tolerant solutions on AWS and/or GCP in a multi cloud ecosystem. Apply modern distributed system design patterns when suitable, including architectural sagas, Command Query Responsibility Segregation (CQRS), event-driven architectures, publish-subscribe models, and point-to-point messaging. Required Skills & Experience: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a closely related discipline.8+ years of professional experience in Machine Learning, Software Engineering, or a related field, with at least 3+ years focused on designing and delivering AI/ML solutions in production environments.1+ year of hands-on experience with Generative AI and Agentic AI solutions, including RAG architectures, semantic search, embedding models, and representation and generative models such as BERT and GPT.5+ years of strong programming experience in Python, including at least 3+ years building production services using FastAPI, Asyncio, and Pydantic.1+ year of experience working with single- and multi-agent frameworks such as LangChain, LangGraph, or CrewAI, and familiarity with state-of-the-art commercial and open-source foundation models.2+ years of hands-on experience with cloud-based GenAI and AI platforms, including AWS SageMaker and Bedrock, Google Vertex AI, Vertex AI Search, and Vertex AI RAG Engine.2+ years of experience setting up and managing Jupyter environments, AutoML workflows, experimentation tracking, model serving, and monitoring on cloud-based MLOps platforms.3+ years of experience delivering production-grade APIs and microservices using modern software engineering practices.2+ years of hands-on experience building DevOps and CI/CD pipelines (e.g., Jenkins or similar), managing cloud deployments using infrastructure as code (Terraform), and collaborating through GitHub.3+ years of experience applying software engineering best practices and architectural patterns, including SOLID principles, 12-Factor App methodology, inversion of control (IoC), and sagas.1+ year of experience with identity and access management solutions, including OAuth 2.1 and OpenID Connect (OIDC).3+ years of hands-on experience with ML and AI frameworks and libraries such as PyTorch, TensorFlow, Keras, scikit-learn, Hugging Face, LangChain, NumPy, and Pandas.3+ years of experience in traditional machine learning, including feature engineering, exploratory data analysis (EDA), model training, and hyperparameter tuning using techniques such as XGBoost, GLMs, KNN, PCA, and SVM.2+ years of experience designing, building, and deploying end-to-end data, ML, and RAG pipelines.2+ years of experience working in lean, agile environments using Scaled Agile Framework (SAFe) or similar methodologies.1+ year of experience using DevSecOps tools such as Nexus, SonarQube, Checkmarx, and mcp-scan.3+ years of demonstrated ability to communicate complex technical concepts to both technical and non-technical audiences and influence leadership decisions. 2+ years of experience mentoring and developing junior AI engineers or data engineers.3+ years of experience collaborating across teams, making informed technical decisions, resolving conflict, and building strong working relationships.2+ years of experience providing AI thought leadership, applying evolving industry design patterns, and aligning technical deliverables with departmental and enterprise strategies.Demonstrated ability to plan, organize, and execute work effectively in fast-paced environments, showing 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 using IaC (Terraform), Cloud Build and/or Cloud Formation. Knowledge on automated testing, validation gates, canary deployments, and rollback strategies for ML and Agentic AI systems. Experience with designing and operationalizing distributed open source inference/hosting platforms using libraries like LLM-D, vLLM, GKE inference gateway. Experience with designing and implementing data pipelines supporting ML and Agentic AI applications (e.g., Snowflake, Airflow, S3/Glue/EMR/Redshift, Apache Iceberg or equivalent). Experience with semantic data layer, semantic enrichment and GenBI. Understanding data structures, big data technologies (i.e. Hadoop, Spark, Hive, etc.), NoSQL and RDBMS. Contribute to guardrails, red-teaming exercises, and build security controls for PII, toxicity, and prompt injection risks. Develop bias detection and explainability tools and SDKs in close collaboration with Data Science teams, utilizing techniques such as LIME, SHAP, and counterfactuals. Partner with governance, risk, compliance, and security teams to align solution using disparate impact remover, model debiasing and counterfactual testing. What We OfferCollaborative work environment with global teams.Competitive salary and comprehensive benefits.Continuous learning and professional development opportunities.