Subject Matter Expert - Insurance at digital wealth management service startup · Bengaluru (Bangalore) · 3 - 5 years · ₹6L - ₹8L / yr · Posted 17 Oct 2022

Subject Matter Expert also referred to as SME, is a person who has special skills or knowledge on a particular job or topic. An SME is considered an expert on a certain topic – not only educated on the subject but has the capacity to share their knowledge with other team members.
Qualification for SME Insurance:
- 3-5 experience in the insurance industry
- Experience in insurance products (all products including life, health & general) and processes and the insurance industry chain
- Bachelor’s degree (or equivalent)
- English: Fluent
Roles and responsibilities
- Strong Process & product knowledge of the Insurance products
- Keeping up-to-date with industry developments. Offer solutions and suggestions for process and product improvement to management.
- Responsible for facilitating OJT for the new joiners on all Insurance related products and processes.
- Help in designing a best-in-class customer support and engagement journey for insurance products.
- Liaise with different stakeholders/partners for quicker resolution of customer inquiries and requests.
- Ability to take ownership and work for early resolution and closure of customer issues.
- Ability to accept change and make the best of any situation.
- Proactive in highlighting/ escalating issues/ concerns/ Areas Of Improvements/ opportunities to the leadership team to get their help as and when needed
- Handling and monitoring of all complaints received through all mediums through tracking in defined TATs
- Ensure that complaint closure quality (RCA) is tracked closely by ensuring first understanding all the issues highlighted by the customer. Maintenance of all RCAs and process improvements undertaken for relevant complaints
- Keeping up to date with all the regulatory requirements and changes in the Insurance industry

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Company Summary :
As the recognized global standard for project-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one-of-a-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America's Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. www.deltek.com
Position Responsibilities :
About the Role
We are seeking a highly motivated AI Solutions Engineer to join Deltek’s growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications.
The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect.
Key Responsibilities
AI & Machine Learning Development
- Design, build, train, evaluate, and deploy machine learning and deep learning models.
- Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral.
- Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks.
- Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions.
- Optimize model performance, scalability, latency, and cost.
Software Engineering & Solution Development
- Develop production-grade AI applications using Python and modern software engineering practices.
- Build APIs, microservices, and AI-powered enterprise applications.
- Integrate AI services with enterprise systems, business applications, and data platforms.
- Apply coding standards, automated testing, CI/CD, and version control best practices.
MLOps & AI Operations
- Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management.
- Automate model training, validation, testing, and deployment processes.
- Monitor model performance, data drift, hallucinations, and operational metrics.
- Support continuous improvement and reliability of AI platforms.
Cloud & Platform Engineering
- Develop AI solutions on Azure, AWS, or Google Cloud platforms.
- Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies.
- Build scalable architectures supporting enterprise AI workloads and real-time inference.
AI Governance & Security
- Ensure compliance with Responsible AI, security, privacy, and regulatory requirements.
- Implement model governance, explainability, bias mitigation, and risk management practices.
- Maintain standards for secure design, deployment, and operation of AI solutions.
Required Qualifications
Education
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field.
Experience
- 5+ years of software engineering or machine learning development experience.
- 2+ years of hands-on experience developing and deploying Agentic AI, Generative AI or AI/ML solutions in production environments.
Technical Skills
Programming & Engineering
- Strong expertise in Python.
- Experience with Java, ReactJS, JavaScript, or similar programming languages.
- Solid understanding of algorithms, data structures, APIs, and software design principles.
Artificial Intelligence & Machine Learning
- Machine Learning and Deep Learning concepts and frameworks.
- Model training, evaluation, optimization, and deployment.
Generative AI
- Large Language Models (LLMs) & SLMs
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- AI Agents and Agentic Workflows
- Fine-tuning and model customization
- Vector embeddings and semantic search
Frameworks & Tools
- PyTorch, TensorFlow, Scikit-learn
- LangChain, LlamaIndex, Semantic Kernel, MCP, A2A and Transformers
- FastAPI, Flask
Data & Analytics
- SQL and NoSQL databases
- Data pipelines, ETL, and data modeling
- Experience with AWS, Azure and Google
MLOps & DevOps
- MLflow, Kubeflow, Azure ML, SageMaker
- Docker and Kubernetes
- Git, GitHub, Azure DevOps, Jenkins
- CI/CD automation and model monitoring
Cloud Platforms
- AWS (preferred)
- AWS Bedrock or Azure OpenAI Service
- AWS SageMaker
- Google Vertex AI
Preferred Qualifications
- Experience designing enterprise-scale AI platforms and products.
- Knowledge of multi-agent architectures and autonomous AI systems.
- Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.
- Understanding of AI governance, compliance, and Responsible AI frameworks.
- Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.
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It would be a Challenging, grounds up a promising circumstance for you. You get an opportunity to chip away at great technologies alongside stunning specialists with strong financial strength.
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