Senior AI Engineer, Assurant, GCC-India
The Senior AI Engineer is a hybrid expert in machine learning, software engineering, and AI infrastructure. This role leads to the development of scalable, modular, and reusable AI systems for enterprise deployment. Serving as a technical leader, this role partners with scientists and architects to shape reliable AI solutions, drive delivery excellence with measurable business value, and enable innovation across teams
This position will be in Bangalore at our India location.
Working Hours: 2 PM IST to 11 PM IST
What will be my duties and responsibilities in this job?
Business Alignment & AI Solution Scoping (10%)
- Engage with product and business stakeholders to identify high-value AI/ML opportunities.
- Translate business objectives into AI solution designs, model success metrics, and technical strategies.
- Prioritize use cases based on feasibility, data readiness, ROI, and ethical considerations.
- Lead end-to-end model lifecycle: scoping, development, testing, deployment, monitoring, and re-training
Data Engineering & Feature Pipeline Development (10%)
- Collaborate with data engineers to design and build robust data pipelines (batch and streaming) to collect and curate large-scale structured and unstructured datasets.
- Engineer high-quality features for AI models using advanced data processing technologies including signal processing, OCR, NLP techniques, or image preprocessing.
- Ensure reproducibility, auditability, and traceability of training data.
AI Model Development & Optimization (15%)
- Design and implement quality AI/ML models using state-of-art AI technologies such as deep learning, generative models, graph neural networks, and reinforcement learning.
- Train, validate, and fine-tune models for production-grade deployment using frameworks like LangChain, TensorFlow, PyTorch, Nemo, or Hugging Face.
- Build reusable, modular components for faster experimentation and deployment to raise solution time-to-market.
- Optimize models and systems for latency, throughput, and cost in production environments.
Integration, Deployment, MLOps & Infrastructure Automation (20%)
- Develop APIs, microservices, and MCPs to integrate AI models into enterprise platforms and user-facing applications.
- Collaborate with AI Architects to design scalable, real-time or batch AI workflows aligned to business requirements using advanced technologies (e.g. CI/CD, caching, model versioning).
- Automate model deployment pipelines using MLOps tools (e.g. MLflow) and container orchestration platforms (e.g. Docker, Kubernetes, GitHub Actions, Terraform).
- Ensure robust monitoring, version control, and rollback mechanisms for safe, repeatable AI deployments.
- Operationalize models using AIOps or MLOps pipelines for training, testing, deployment and scaling (CI/CD, containerization, orchestration).
- Build scalable APIs or model endpoints with low-latency inference using cloud-native tools and managed AI platforms (e.g. Azure AI, Databricks).
- Monitor system performance, latency, cost, and retraining needs in production environments.
- Manage model registry, lineage, and lifecycle across deployment stages.
Model Monitoring & Continuous Learning (10%)
- Implement end-to-end monitoring framework to ensure single tracking-and-tracing process from business KPI, data quality and drift, to model performance.
- Define retraining strategies, thresholds, and automation policies based on real-time or scheduled triggers.
- Develop feedback loops including autonomous learning loop and optimized Human-in-the-Loop to enable model retraining and adaptation based on user behavior or new data.
- Track performance in production (accuracy, bias, latency) and manage model lifecycle in alignment with compliance needs.
Governance, Ethics & Compliance (5%)
- Ensure AI models meet ethical AI guidelines, explainability requirements, and regulatory standards (e.g., GDPR, HIPAA, model fairness).
- Document AI decision logic, limitations, risks, and assumptions for internal and external stakeholders.
Research & Innovation (20%)
- Explore and prototype cutting-edge AI methodologies to drive innovation in core products and internal capabilities.
- Stay current with academic and industry advancements in AI and contribute to technical knowledge sharing within the team.
Project Management & Collaboration (10%)
- Lead or contribute to cross-functional AI projects across the full lifecycle, including scope definition, delivery tracking, risk mitigation, and stakeholder communication.
- Collaborate across product, engineering, design, and operations to ensure successful AI integration and delivery.
- Mentor junior engineers, enforce code quality, and lead technical delivery reviews.
What are the requirements needed for this position?
- Bachelor’s or Master’s degree in computer science, Applied Mathematics, or a related technical field; PhD preferred.
- 4–7 years of hands-on experience building and deploying production-grade AI/ML systems, including LLMs or GenAI pipelines.
- Strong software engineering background and experience working in production-grade AI/ML environments.
- Deep Expertise in C++/CUDA, Python/PySpark, Java/Scala, cloud-native AI/LLM/ML tools (Azure, AWS, GCP), DL/LLM/ML frameworks (LangChain, TensorFlow, PyTorch, OpenCV, Hugging Face), and AIOps platforms.
- Deep Understanding of AI/ML algorithms including Supervised/Unsupervised Learning, Time Series, Neural Network, Transformers, Bayesian Inference, Reinforcement Learning, BERT/CLIP.
- Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs).
- Experience in building distributed, real-time inference systems and optimizing inference at scale.
- Proficiency in model evaluation, distributed training, and hyperparameter optimization.
- Strong communication, mentoring, and architectural documentation skills.
- Ability to work independently in fast-paced, cross-functional environments.
What are the Preferred requirements needed for this position?
- Experience in regulated industries (e.g., finance, healthcare, insurance)
- Excellent communication and stakeholder engagement skills
- Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
- Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
- Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
- Experience with GenAI, vector databases, graph databases, and agent-based AI systems
- Proficient in API, MCP and Microservices technologies
- Strong hands-on experience in tools like MLflow, Kubeflow, Ray, LangChain, LangGraph, or Airflow
- Track records in large-scale, real-time AI/GenAI/ML database and solution technologies
- Experience with one or more applied domains is a plus: language model, computer vision, signal processing, generative AI, Digital Twins, optimization programming, recommendation systems, or autonomous agents
- Background in responsible AI, model interpretability, and fairness auditing
Helping People Thrive in a Connected World
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What’s the culture like at Assurant?
Our unique culture is a big reason why talented people choose Assurant. Named a Best/Great Place to Work in 14 countries and awarded the Fortune America’s Most Innovative Companies recognition, we bring together top talent around the world. Although we have a wide variety of skills and experiences, we share common characteristics that are uniquely Assurant. A passion for service. An ability to innovate in practical ways. And a willingness to take chances. We call our culture The Assurant Way.
Company Overview
Assurant is a leading global business services company that supports, protects, and connects major consumer purchases. A Fortune 500 company with a presence in 21 countries, Assurant supports the advancement of the connected world by partnering with the world’s leading brands to develop innovative solutions and deliver an enhanced customer experience through mobile device solutions, extended service contracts, vehicle protection services, renters insurance, lender-placed insurance products, and other specialty products.
AI and Biometric Usage
Assurant supports the responsible use of Artificial Intelligence (AI), but we want to know the real you. Visit our AI Usage Guidelines page to understand what we expect from applicants regarding their use of AI during the application process. Since we would like to know the real you, we require that all of our virtual interviews be conducted on video.
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Equal Opportunity Statement
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