Description

• Review data preparation tasks, and plans to address patterns or anomalies, while ensuring data readiness for advanced modeling and AI.
• Review models for complex use cases (e.g., forecasting models, LLM-based solutions), and refine algorithms to meet business needs.
• Review plan for smooth deployment into scalable, production-ready solutions.
• Review test plans and test results for analytics use cases, while defining optimization standards for model accuracy and stability, in alignment with business goals.
• Build models and analytics solutions tailored to business needs.
• Ensure quality and scalability across client engagements while actively contributing to knowledge assets and innovation streams.
• Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions.
• Review and refine analytics problems; identify data sources and extract from diverse environments.
• Oversee analysis execution and drive business insights.
• Create monitoring strategies across multiple projects, embedding governance frameworks to ensure robustness, reliability, and risk awareness.
• Review monitoring frameworks, refine documentation/reporting templates, and present insights on anomalies or slippages to stakeholders.
• Refine documentation strategy across teams, ensuring transparency and reproducibility of complex analytics solutions.
• Collaborate with cross-functional teams, ensuring alignment between analytics delivery and business strategy.
• Review analytics outputs for adherence to quality frameworks and project commitments.
• Recommend improvements to quality metrics and guide team members to align with standards.
• Identify and recommend model changes needed for successful deployment.
• Engage in creation and refinement of IP assets such as analytics prototypes and accelerators.
• Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking.
• Review innovative models and applications in non-ML, ML, deep learning, or LLM areas.
• Support participation in forums and internal knowledge exchanges.
• Deliver training sessions on technical and analytics-specific topics.
• Collaborate on content creation and mentor team members through hands- on guidance in live projects.
• Provide input for segment and unit-level business plans.
  
   

Your Contribution To The Team

 

• A strong focus on innovation and scalable analytics solutions.
• Proactive problem-solving ability for complex, data-driven business challenges.
• Deep technical expertise across advanced modeling and AI use cases.
• A strategic mindset to align analytics with business goals.
• Ability to mentor team members and drive continuous improvement.
• Strong communication and knowledge-sharing capabilities.
  
   

Required Skill And Experience

 

• Enterprise GenAI and Agentic AI solutions across RAG, AI agents, conversational AI, enterprise search, workflow automation, document intelligence, and AI copilots; comfortable with planner-executor, reflection, multi-agent, and graph-based orchestration patterns.
• Hands-on with orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI) and vector databases (Pinecone, Weaviate, Milvus, pgvector, FAISS, ChromaDB, Azure AI Search); working knowledge of grounding, prompt engineering, and context management.
• Experience integrating GenAI with Azure OpenAI, AWS Bedrock, Vertex AI, OpenAI, Anthropic, and Gemini, along with enterprise APIs, middleware, and data platforms.
• Command of AI governance, LLMOps, evaluation, observability, guardrails, model safety, compliance, and cloud-native deployment.
• Ability to define reference architectures, lead solutioning discussions, drive architecture reviews, and collaborate with enterprise architects, business stakeholders, and engineering teams.
  
   

Preferred Skill And Experience

 

• Exposure to open-source LLM ecosystems — Hugging Face, PyTorch, LoRA, QLoRA, PEFT — and models such as Llama, Mistral, Gemma, DeepSeek, and Falcon.
• Familiarity with multimodal AI, including vision-language models, speech and audio models, and image or video generation.
• Familiarity with DevOps and IaC tooling (GitHub Actions, Jenkins, Terraform, Helm, Kubernetes) and awareness of front-end stacks (React, Angular, TypeScript, GraphQL) used in copilot interfaces

Key Skills

Education

Bachelor's degree