Posted:1 week ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Key Responsibilities

Architecture Solution Design

  • Architect end to end data platforms, AI/ML systems, and GenAI solutions across cloud environments.
  • Design scalable data pipelines, lakehouses, warehouses, and real time processing architectures.
  • Define reference architectures, best practices, and reusable frameworks for Data AI delivery.
  • Ensure solutions meet performance, security, governance, and compliance requirements.

Technical Leadership

  • Provide architectural oversight to engineering teams across data engineering, ML, and GenAI projects.
  • Review solution designs, code, and deployment pipelines to ensure technical quality.
  • Guide teams on modern data stacks, cloud-native patterns, and AI/ML engineering practices.
  • Mentor engineers and analysts to strengthen Data AI capabilities.

Client Engagement Presales

  • Work with sales and presales to craft solution architectures, proposals, and technical presentations.
  • Engage with client architects, product owners, and C suite stakeholders to understand business needs.
  • Translate business challenges into scalable, outcome driven Data AI solutions.
  • Support estimation, scoping, and technical risk assessment.

Delivery Excellence

  • Oversee implementation of data platforms, ML models, and GenAI workflows.
  • Ensure adherence to architectural standards, data quality, and engineering best practices.
  • Drive performance optimization, cost efficiency, and reliability across deployed systems.
  • Establish CI/CD, MLOps, and LLMOps pipelines for production grade deployments.

Innovation Thought Leadership

  • Evaluate emerging technologies across AI/ML, GenAI, LLMOps, and cloud data platforms.
  • Build accelerators, reusable components, and architectural blueprints.
  • Contribute to internal knowledge sharing, blogs, whitepapers, and tech talks.

Technical Skills Expertise

Data Engineering Analytics

  • Strong expertise in SQL, ETL/ELT, data modeling, and pipeline orchestration.
  • Experience with lakehouse and warehouse platforms (Databricks, Snowflake, Redshift, BigQuery).
  • Hands on with PySpark, Python, Scala, and distributed data processing.

AI/ML

  • Experience in feature engineering, model development, evaluation, and optimization.
  • Familiarity with ML frameworks: TensorFlow, PyTorch, Scikit learn, XGBoost, LightGBM.
  • Applied ML experience in forecasting, NLP, classification, clustering, and anomaly detection.

GenAI LLM Ecosystems

  • Experience designing RAG architectures and LLM powered applications.
  • Knowledge of vector databases (Pinecone, Qdrant, FAISS, Chroma).
  • Familiarity with multi agent frameworks (LangGraph, CrewAI, AutoGen).
  • Strong understanding of embeddings, prompt engineering, fine tuning, and document intelligence.

Cloud Platforms

  • Hands on experience with at least one major cloud:
    • Azure: Azure AI, OpenAI, Data Factory, Synapse, Databricks
    • AWS: Bedrock, Glue, Athena, S3, SageMaker
    • GCP: Vertex AI, BigQuery, Dataflow

MLOps LLMOps

  • CI/CD for ML and GenAI pipelines.
  • Model deployment using Docker, Kubernetes, and serverless patterns.
  • Monitoring for drift, accuracy, hallucination checks, and model lifecycle management.

Requirements

  • Bachelor s degree in Engineering, Computer Science, or related field.
  • 10 15 years of experience in Data Engineering, AI/ML, or Cloud Data Architecture.
  • Proven experience architecting enterprise scale data and AI solutions.
  • Strong understanding of cloud-native architectures and modern data stacks.
  • Experience working with cross functional teams in a matrix environment.
  • Excellent communication and stakeholder engagement skills.
  • Ability to translate business needs into robust technical architectures.
  • Strong problem solving mindset with a focus on scalability, security, and performance.

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