SQL and Python Engineers

Req ID: 368400\n\nNTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.\n\nWe are currently seeking a SQL and Python Engineers to join our team in Remote, Karn\u0101taka (IN-KA), India (IN).\n\nJob Description\n\n### AI Engineer (Generative AI / MLOps / AI Agents) \n\n \nLocation: [City, State] | Hybrid \nEmployment Type: Contract (6\u201312 months, with potential for extension) \n \n\n\nPosition Overview\n\nWe are seeking a skilled and motivated AI Engineer (Mid-Level) to join us. This role sits at the intersection of Generative AI, MLOps, and Intelligent Agent development \u2014 and is responsible for designing, building, and deploying AI-powered solutions that directly support our P\u0026C insurance operations.\n\nYou will work closely with our data engineering, analytics, and business teams to deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines across claims, underwriting, and actuarial domains. This is a hands-on, delivery-focused role for an engineer who is comfortable moving from architecture whiteboard to working code.\n\nKey Responsibilities\n\nGenerative AI \u0026 LLM Engineering\n\n\u2022 Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q\u0026A.\n\n\u2022 Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.\n\n\u2022 Develop prompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts.\n\n\u2022 Integrate LLM capabilities with internal data platforms via LangChain, LlamaIndex, or Semantic Kernel.\n\n\u2022 Evaluate and benchmark foundational models (OpenAI GPT-4o, Azure OpenAI, Claude, Mistral, Llama) against insurance-specific tasks to guide platform selection.\n\nAI Agents \u0026 Automation\n\n\u2022 Architect and implement autonomous AI agents capable of multi-step reasoning, tool use, and decision-making for workflows such as FNOL triage, claims routing, policy lookup, and compliance checks.\n\n\u2022 Build agentic frameworks using patterns such as ReAct, Chain-of-Thought, and Tool-Augmented Agents to handle complex, multi-turn insurance workflows.\n\n\u2022 Design human-in-the-loop (HITL) checkpoints and escalation logic to ensure AI agents operate within defined risk and compliance boundaries.\n\n\u2022 Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools such as Azure Logic Apps, Apache Airflow, or Databricks Workflows.\n\n\u2022 Develop guardrails, monitoring, and audit logging for all deployed agents to meet regulatory and governance standards.\n\nMLOps \u0026 Model Deployment\n\n\u2022 Build and maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring using MLflow, Azure ML, and Databricks.\n\n\u2022 Implement CI/CD pipelines for ML models using Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.\n\n\u2022 Deploy models as REST APIs or batch inference services on Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low-latency response.\n\n\u2022 Establish model monitoring frameworks to detect data drift, model degradation, and prediction anomalies in production.\n\n\u2022 Manage the model registry and lineage tracking to maintain governance and auditability of all AI assets.\n\n\u2022 Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with the Feature Store on Databricks or Azure ML.\n\nCollaboration \u0026 Delivery\n\n\u2022 Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs.\n\n\u2022 Participate in Agile/Scrum ceremonies including sprint planning, standups, and retrospectives as an active delivery contributor.\n\n\u2022 Produce clear, well-structured technical documentation including solution designs, API specs, model cards, and deployment runbooks.\n\n\u2022 Mentor junior engineers and contribute to internal AI engineering best practices and standards.\n\nRequired Qualifications\n\nEducation\n\n\u2022 Bachelor\u0027s degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master\u0027s degree is a plus.\n\nExperience\n\n\u2022 3\u20135 years of professional experience in AI/ML engineering, with demonstrated delivery of production-grade AI systems.\n\n\u2022 Hands-on experience building and deploying LLM-powered applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.\n\n\u2022 Proven experience implementing MLOps pipelines in cloud environments (Azure preferred).\n\n\u2022 Experience developing AI agents or automation workflows using agentic frameworks.\n\n\u2022 Prior experience in financial services, insurance, or regulated industries is strongly preferred.\n\nTechnical Skills\n\nGenerative AI \u0026 LLMs\n\n\u2022 OpenAI / Azure OpenAI (GPT-4o, GPT-4 Turbo), Claude, Mistral, or open-source LLMs (Llama 3, Falcon)\n\n\u2022 RAG architectures, vector search, embeddings (OpenAI, Cohere, SentenceTransformers)\n\n\u2022 LangChain, LlamaIndex, Semantic Kernel\n\n\u2022 Prompt engineering, few-shot learning, instruction tuning, RLHF concepts\n\nAI Agents \u0026 Automation\n\n\u2022 Agentic frameworks: ReAct, Tool-Augmented Agents, LangGraph, AutoGen, CrewAI\n\n\u2022 Workflow orchestration: Apache Airflow, Databricks Workflows, Azure Logic Apps\n\n\u2022 API design and integration: REST, GraphQL, Webhooks\n\nMLOps \u0026 Model Serving\n\n\u2022 MLflow (experiment tracking, model registry, model serving)\n\n\u2022 Azure Machine Learning, Databricks AutoML \u0026 Feature Store\n\n\u2022 Docker, Kubernetes (AKS), Azure Container Apps\n\n\u2022 CI/CD: Azure DevOps, GitHub Actions\n\n\u2022 Model monitoring: Evidently AI, Azure ML monitoring, or equivalent\n\nProgramming \u0026 Data Engineering\n\n\u2022 Python (expert level): PyTorch, Hugging Face Transformers, scikit-learn, Pandas, NumPy\n\n\u2022 PySpark and Delta Lake for large-scale data processing\n\n\u2022 SQL (T-SQL / Spark SQL) for feature engineering and data validation\n\n\u2022 Git for version control and collaborative development\n\nCloud \u0026 Platform\n\n\u2022 Microsoft Azure (Azure OpenAI, Azure AI Search, AKS, Azure Data Factory, Azure Key Vault)\n\n\u2022 Databricks (Unity Catalog, Delta Live Tables, Workflows)\n\n\u2022 Microsoft Fabric / OneLake (familiarity a strong plus)\n\nPreferred Qualifications\n\n\u2022 Experience with P\u0026C insurance workflows such as FNOL processing, claims triage, underwriting decisioning, or actuarial modeling.\n\n\u2022 Familiarity with insurance regulatory requirements including NAIC guidelines and data privacy standards (CCPA, GDPR).\n\n\u2022 Experience implementing responsible AI principles \u2014 fairness, explainability, and bias mitigation \u2014 in regulated environments.\n\n\u2022 Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred.\n\n\u2022 Exposure to Data Mesh patterns and publishing AI model outputs as domain data products.\n\n\u2022 Familiarity with Databricks Model Serving and Mosaic AI capabilities.\n\nAbout NTT DATA\n\nNTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world\u0027s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R\u0026D.\n\nWhenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client\u2019s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.\n\nNTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you\u0027d like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.\n