Senior Decision Scientist (Gen AI)

Role Overview :We are seeking a Senior Decision Scientist with 6–8 years of experience to design and build enterprise-grade Generative AI solutions. The role focuses on developing and deploying LLM-based applications, including RAG architectures, fine-tuning strategies, and context management using MCP. The candidate will work with secure, scalable enterprise LLM platforms, collaborate with cross-functional teams, and deliver production-ready AI systems. Experience in predictive or forecasting ML models is a strong added advantage.What your main responsibilities are:Design, develop, and deploy LLM-powered applications for enterprise use casesBuild and optimize RAG pipelines, including: Vector databases and embeddings Document ingestion, chunking, indexing, and retrieval strategies Implement and evaluate fine-tuning approaches (SFT, LoRA, adapters, PEFT techniques) Work with enterprise LLM platforms ensuring:Data security, privacy, and compliance Token efficiency, latency optimization, and cost control Integrate MCP (Model Context Protocol) or equivalent approaches to manage model context, tool usage, and structured prompts Develop robust prompt engineering and prompt orchestration strategies Design scalable APIs and microservices to serve GenAI workloads Evaluate open-source and commercial LLMs (OpenAI, Azure OpenAI, Anthropic, open source models, etc.)Collaborate with MLOps and Platform teams on:Model monitoring and observability Versioning, rollback, and lifecycle management Partner with business and product stakeholders to translate requirements into production ready GenAI solutionsWhat we are looking for Required Qualifications:6–8 years of overall software / data / ML experience Strong hands-on experience with LLMs and GenAI frameworks Proven experience building RAG-based systems in production Solid understanding of LLM fine-tuning techniques Experience working with enterprise LLM deployments (private endpoints, governance, security controls) Strong proficiency in Python Experience with libraries such as LangChain, LlamaIndex, Transformers, or similar Familiarity with vector databases (e.g., FAISS, Pinecone, Milvus, Weaviate, Azure AI Search) Good understanding of REST APIs, microservices, and cloud-native architectures Enterprise & Platform Understanding Experience handling enterprise data (structured & unstructured) Understanding of data privacy, compliance, and responsible AI practices Experience deploying models on cloud platforms (Azure, AWS, or GCP)Good to Have / Added Advantage Experience with ML predictive modeling or time-series forecasting Exposure to traditional ML workflows (feature engineering, model evaluation, deployment) Experience with MLOps tools and pipelines Familiarity with search relevance, ranking models, or recommendation systems Experience mentoring junior engineers or leading technical initiatives

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