Senior Associate - AI

Department: IT

Employment Type: Permanent - Full Time

Location: Pune

Description

Key Responsibilities

• Assemble, cleanse, and model complex datasets from across the Group to meet functional and non-functional business requirements, using Azure tool chain and any other relevant tools.
• Design, build, evaluate, and deploy machine learning and AI solutions for business-focused use cases including claims triage, fraud detection, outcome prediction, document processing, conversational AI, and operational efficiency optimisation.
• Collect and structure data through a combination of business analysis, study design, and automated collection pipelines — identifying the most valuable signals within complex datasets.
• Build and maintain analytical tools and reporting solutions that provide stakeholders with actionable insight into key business performance metrics, including operational efficiency, claims outcomes, and customer experience.
• Create clear, compelling reports, dashboards, and presentations that translate complex data findings into simple, actionable recommendations for business, product, and executive audiences.
• Develop and implement robust evaluation frameworks for machine learning and generative AI systems, including offline testing, model validation, performance monitoring, and continuous improvement in production environments.
• Partner with data engineers, software engineers, AI engineers, and platform teams to deliver secure, scalable, and maintainable production AI solutions.
• Contribute to the continuous improvement of data science practice at Davies, including tooling, methodology, model governance, and knowledge sharing across the AI Office and wider Group.
• Design and implement AI-enabled services and microservices using modern software engineering practices, APIs, cloud-native architectures, and containerised deployment patterns.

Skills, Knowledge and Expertise

• Expert-level Python development skills for AI applications, machine learning, data processing, API development, and automation, with strong command of libraries such as pandas, scikit-learn, PyTorch, Pydantic, and related ecosystems.
• Experience with MLOps and LLMOps practices including CI/CD pipelines, model versioning, experiment tracking, automated evaluation, deployment automation, observability, and governance.
• Hands-on experience designing, building, and evaluating LLM-based applications and generative AI systems, including prompt engineering, RAG architectures, embedding models, vector databases, structured outputs, function/tool calling, agentic workflows, context management, evaluation frameworks, and safety/guardrail implementation. Experience with frameworks and platforms such as Azure OpenAI, OpenAI APIs, LangChain, LangGraph, Semantic Kernel, or equivalent.
• Strong software engineering skills including API design and implementation using FastAPI, asynchronous programming, testing, dependency management, versioning, observability, authentication, and integration of AI services into production systems.
• Experience delivering solutions using Microsoft Azure services including Azure OpenAI, Azure AI Foundry, Azure Data Factory, Microsoft Fabric, Azure Functions, Azure Storage, and related cloud-native services.
• Experience designing and evaluating machine learning models — including classification, regression, clustering, and NLP techniques — and deploying them in production or near-production environments.
• SQL skills and experience working with relational databases, data warehouses, and medallion architecture data lake patterns.

• Excellent written and verbal communication skills, demonstrated through the preparation of reports, presentations, and technical documentation for diverse audiences including senior leadership.
• Analytical and problem-solving mindset with exceptional attention to detail — comfortable searching through large, messy datasets to surface usable and statistically meaningful insight.
• Ability to work independently and collaboratively across teams with different technical backgrounds, translating between data science concepts and business requirements with clarity.
• Experience applying data science in insurance, financial services, claims management, or other regulated industries is a strong advantage, particularly where data governance and model explain ability are key requirements.

• Very Strong Python (for all the requirements below)
• Knowledge of NLP techniques and how to do development with LLM
• Experience designing and deploying microservices and AI services using FastAPI, Docker, REST APIs, dependency management, testing frameworks, and cloud-native deployment patterns.
• Understand how to work with data - cleansing, insights collection, iterative development, storage options
• Experience taking AI systems from proof-of-concept to production.