Position: Sr. Data Analyst Experience: 5 - 9 years Location: Pune / Bangalore Job Description Summary: Key Skills : Strong SQL, Python, Pyspark, Jupyter Notebook, Agile / Scrum / Jira / Confluence, Microsoft Excel, Exposure to any cloud (GCP / AWS / Azure) would be a plus Job Description: Must-Have: 5 - 9 years of professional experience as a Data Analyst with good decision-making, analytical and problem-solving skills. Working knowledge / experience of Big Data frameworks like Hadoop, Hive and Spark. Hands-on experience in query languages like HQL or SQL (Spark SQL) for Data exploration. Data mapping: Determine the data mapping required to join multiple data sets together across multiple sources. Documentation - Data Mapping, Subsystem Design, Technical Design, Business Requirements. Exposure to Logical to Physical Mapping, Data Processing Flow to measure the consistency, etc. Data Asset design / build: Working with the data model / asset generation team to identify critical data elements and determine the mapping for reusable data assets. Understanding of ER Diagram and Data Modeling concepts Exposure to Data quality validation Exposure to Data Management, Data Cleaning and Data Preparation Exposure to Data Schema analysis. Exposure to working in Agile framework. SQL, Pyspark, Python with Banking Domain knowledge / Credit & Lending domain knowledge. Knowledge of Credit Risk Frameworks such as Basel II, III, IFRS 9 and Stress Testing and understanding their drivers - advantageous Retail Credit / Traded Credit knowledge - applications will be considered Good To Have: BFSI Domain knowledge Data Visualization - Tableau or Qlik Sense Exposure to Hadoop, Hive and ETL. Working knowledge of any cloud services like AWS or GCP or Azure. Any relevant certifications would be a plus. Role & Responsibilities: Take complete responsibility for the sprint stories' execution. Understand the business requirements from the product/project stakeholders and break the requirements into simpler stories and tasks and do the necessary mapping of the tasks to the logical model of the solutions. Mapping of business entities to technical attributes with the logic for transformation defined clearly. Be accountable for the delivery of the tasks in the defined timelines with good quality. Follow the processes for project execution and delivery. Follow agile methodology. Working with the team leads closely and contribute to the smooth delivery of the project. Understand/define the architecture and discuss the pros-cons of the same with the team. Involve in the brainstorming sessions and suggest improvements in the architecture/design. Working with other teams leads to getting the architecture/design reviewed. Keep all the stakeholders updated about the project, task status, risks, and issues if any.
Preferred Skills (Bonus Points): Familiarity with multi-agent orchestration frameworks . Experience in human-in-the-loop AI , knowledge extraction , or clinical/pharma R&D . Knowledge of Responsible AI principles , red teaming , or AI risk assessment . Strong backend development experience with emphasis on code quality , logging , and automated testing . Exposure to semantic search , vector databases , or embedding optimization techniques . Why Join Us? Work at the cutting edge of GenAI, multi-agent systems, and enterprise automation . Build impactful AI solutions in domains such as healthcare , life sciences , R&D , and finance . Collaborate with world-class engineers, scientists, and innovators in a high-performance team. Opportunity to define and shape the next-gen AI platforms and infrastructure. About the Role: We are hiring an experienced AI/ML Engineer with strong expertise in Generative AI , LLMOps , and agentic AI systems . This role blends the cutting edge of multi-agent orchestration , LLM pipelines , and MLOps best practices . You will be responsible for designing and deploying autonomous agents powered by LLMs, optimizing GenAI pipelines, and ensuring responsible, secure, and scalable AI operations. Youll work across diverse domains including annotation automation, knowledge graph generation, drug discovery, clinical R&D, and enterprise data orchestration . Key Responsibilities: Design & Develop GenAI Agents: Build intelligent agents using frameworks like LangChain , AutoGen , LangGraph , or Semantic Kernel for tasks such as summarization, labeling, document classification, and data annotation. Orchestrate Multi-Agent Systems: Implement memory/state management, decision-making strategies, and inter-agent communication using LLMs and reinforcement learning. LLMOps & Pipeline Development: Develop end-to-end LLMOps pipelines for fine-tuning, deployment, monitoring, and evaluation of LLMs using MLflow , Azure , GCP , or AWS . Responsible AI & Governance: Enforce compliance with governance frameworks (GDPR, AI Act) and embed explainability, fairness, and transparency into AI systems. Security & Infrastructure: Integrate secure deployment practices, access controls, model sandboxing, and cloud-native CI/CD systems for scalable GenAI products. RAG & Vector Search: Build Retrieval-Augmented Generation (RAG) pipelines using FAISS , Pinecone , or similar tools, and optimize LLM context windows and embeddings. Model Lifecycle & Observability: Automate training, fine-tuning (PEFT, RLHF), rollback, and real-time monitoring for GenAI agents and models. Collaboration & Impact: Work closely with MLOps, DevSecOps, Data Scientists , and product teams to drive real-world GenAI applications in healthcare, pharma, finance, and enterprise data systems. Required Qualifications: Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, or related fields. 36 years of experience in AI/ML Engineering with at least 12 years in GenAI/LLM-based systems . Strong hands-on experience with Python , PyTorch/TensorFlow , and LLM frameworks like LangChain , LlamaIndex , Hugging Face , or AutoGen . Proficiency in cloud platforms: Azure , GCP , or AWS and containerization tools like Docker . Sound understanding of prompt engineering , NLP , Reinforcement Learning , and model evaluation . Experience building or integrating agentic frameworks , LLM pipelines , and AI observability systems . Apply Now if You Have: A passion for building intelligent, autonomous AI systems Proven track record of deploying LLM-based applications at scale A drive to create real-world impact with GenAI