Sql And Python Engineers Job at NTT DATA (Remote)

Job Description

We are currently seeking a SQL and Python Engineers to join our team in Remote, Karnātaka (IN-KA), India (IN). AI Engineer (Generative AI / MLOps / AI Agents). Employment Type: Contract (6–12 months, with potential for extension). We 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 — and is responsible for designing, building, and deploying AI-powered solutions that directly support our P&C insurance operations. You 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.

Job Responsibility

Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q&A
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases to ground LLM outputs in enterprise knowledge bases
Develop prompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts
Integrate LLM capabilities with internal data platforms via LangChain, LlamaIndex, or Semantic Kernel
Evaluate and benchmark foundational models against insurance-specific tasks
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
Build agentic frameworks using patterns such as ReAct, Chain-of-Thought, and Tool-Augmented Agents
Design human-in-the-loop checkpoints and escalation logic
Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools
Develop guardrails, monitoring, and audit logging for all deployed agents
Build and maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring using MLflow, Azure ML, and Databricks
Implement CI/CD pipelines for ML models using Azure DevOps or GitHub Actions
Deploy models as REST APIs or batch inference services on Azure Kubernetes Service or Azure Container Apps
Establish model monitoring frameworks to detect data drift, model degradation, and prediction anomalies
Manage the model registry and lineage tracking
Collaborate with data engineering teams to ensure feature pipelines are production-grade
Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs
Participate in Agile/Scrum ceremonies
Produce clear, well-structured technical documentation
Mentor junior engineers and contribute to internal AI engineering best practices and standards

Requirements

Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field
3–5 years of professional experience in AI/ML engineering
Hands-on experience building and deploying LLM-powered applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel
Proven experience implementing MLOps pipelines in cloud environments (Azure preferred)
Experience developing AI agents or automation workflows using agentic frameworks
Prior experience in financial services, insurance, or regulated industries is strongly preferred
Technical skills: Generative AI & LLMs (OpenAI / Azure OpenAI, Claude, Mistral, or open-source LLMs
RAG architectures, vector search, embeddings
LangChain, LlamaIndex, Semantic Kernel
Prompt engineering, few-shot learning, instruction tuning, RLHF concepts)
AI Agents & Automation (Agentic frameworks: ReAct, Tool-Augmented Agents, LangGraph, AutoGen, CrewAI
Workflow orchestration: Apache Airflow, Databricks Workflows, Azure Logic Apps
API design and integration: REST, GraphQL, Webhooks)
MLOps & Model Serving (MLflow
Azure Machine Learning, Databricks AutoML & Feature Store
Docker, Kubernetes (AKS), Azure Container Apps
CI/CD: Azure DevOps, GitHub Actions
Model monitoring: Evidently AI, Azure ML monitoring, or equivalent)
Programming & Data Engineering (Python expert level: PyTorch, Hugging Face Transformers, scikit-learn, Pandas, NumPy
PySpark and Delta Lake
SQL (T-SQL / Spark SQL)
Git)
Cloud & Platform (Microsoft Azure: Azure OpenAI, Azure AI Search, AKS, Azure Data Factory, Azure Key Vault
Databricks: Unity Catalog, Delta Live Tables, Workflows
Microsoft Fabric / OneLake familiarity a strong plus)
Master's degree is a plus

Nice to have

Experience with P&C insurance workflows such as FNOL processing, claims triage, underwriting decisioning, or actuarial modeling
Familiarity with insurance regulatory requirements including NAIC guidelines and data privacy standards (CCPA, GDPR)
Experience implementing responsible AI principles — fairness, explainability, and bias mitigation — in regulated environments
Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred
Exposure to Data Mesh patterns and publishing AI model outputs as domain data products
Familiarity with Databricks Model Serving and Mosaic AI capabilities

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Sql And Python Engineers

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