Description

Job description The role aims to instill a culture of data aggregation, advanced analytics, and AI-driven insights across the organization. The incumbent will identify patterns in data to improve consumer experience, enhance decision-making, and contribute towards achieving organizational goals and objectives. Key Responsibilities Functional Scale analytics capability across business functions and guide the organizations data strategy. Organize, process, and analyze large, diverse datasets across multiple platforms. Identify and communicate key insights to influence product and business strategy. Collaborate with vendors and partners in a technical capacity on scope, approach, and deliverables. Develop proofs of concept to validate ideas and solutions. Partner with business teams to formalize analytical requirements and design robust, efficient, and reliable reporting solutions. Design and implement advanced statistical testing and models for problem solving. Deliver clear and concise insights to senior leadership, elevating analytics into strategic recommendations. AI/ML & LLM-Specific Build, train, and fine-tune data pipelines for ML and LLM use cases (dataset creation, cleaning, augmentation, labeling workflows). Apply expertise in ML algorithms (e.g., XGBoost, SVM) and statistical modeling techniques (regression, segmentation, forecasting, hypothesis testing, A/B testing, decision trees, etc.). Work hands-on with modern LLMs and embeddings (OpenAI, Anthropic, LLaMA-family, Mistral, etc.). Implement instruction tuning/fine-tuning methods (LoRA, PEFT, adapters). Integrate AI models with backend and frontend systems using APIs, batching, caching, and streaming responses. Develop safe, secure, and compliant AI applications, including safety layers (prompt injection defense, hallucination reduction, PII redaction). Build and deploy scalable AI/ML systems using MLOps practices (Docker, Kubernetes, CI/CD). Work with frameworks and tools such as LangChain, Hugging Face Transformers, Ray/Serve, and vector databases (FAISS, Pinecone, Milvus). Job Requirements Qualifications M.Tech / B.E. / B.Tech / M.Sc. in Computer Science, Statistics, Mathematics, Operational Research, Econometrics, or a related quantitative field. Experience Minimum 1+ years of experience in the analytics / AI / ML domain. Prior exposure to BFSI/NBFC industry is desirable. Functional Competencies Strong analytical, logical reasoning, problem solving, and numerical aptitude Proven experience with large datasets and big data technologies (SQL, Hadoop, Hive). Advanced knowledge of data mining, statistical analysis, and ML algorithms. Hands-on expertise with Python for analytics and AI development. Proficiency in CI/CD, MLOps, and modern AI/ML engineering practices. Familiarity with Azure OpenAI, LangChain, Hugging Face, and related ecosystems. Knowledge of speech-to-text systems (e.g., Whisper, Indian languages a plus). Desirable Skills Experience in BFSI/NBFC domain. Exposure to speech-to-text and multilingual NLP solutions.

Role and Responsibilities

• Key Responsibilities Analytics & Business Impact Scale advanced analytics and AI capabilities across multiple business functions. Identify trends, patterns, and insights from large, complex datasets to influence product and business strategies. Deliver clear, actionable insights and strategic recommendations to senior leadership. Partner with business teams to translate requirements into robust analytical and reporting solutions. Develop proof-of-concept (PoC) solutions to validate AI and analytics use cases. AI / ML & Generative AI Engineering Build, train, and fine-tune data pipelines for ML and LLM-based use cases, including data preparation, augmentation, labeling, and validation. Design and implement machine learning models using techniques such as regression, classification, segmentation, forecasting, hypothesis testing, A/B testing, and decision trees. Apply ML algorithms such as XGBoost, SVM, and ensemble models for business problem-solving. Work hands-on with modern Large Language Models (LLMs) and embeddings (OpenAI, Anthropic, LLaMA-family, Mistral, etc.). Implement instruction tuning and fine-tuning techniques such as LoRA, PEFT, and adapters. AI Application Development & Deployment Integrate AI models with backend and frontend systems using APIs, batching, caching, and streaming responses. Build safe, secure, and compliant AI solutions with safeguards for prompt injection, hallucination reduction, and PII protection. Design and deploy scalable AI/ML systems using MLOps best practices (Docker, Kubernetes, CI/CD pipelines). Utilize modern AI frameworks and tools such as LangChain, Hugging Face Transformers, Ray/Serve, and vector databases (FAISS, Pinecone, Milvus). Collaboration & Vendor Management Collaborate with vendors and external partners on technical scope, architecture, and deliverables. Work cross-functionally with product, engineering, risk, and business teams to ensure AI solutions align with organizational goals.