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Solution Architect ( Python AI with AWS)

10 - 18 years

20 - 35 Lacs

Posted:3 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Must have

  1. Strong proficiency in Python with the ability to write clean, modular, and efficient code for AI/ML workflows and backend services.
  2. Hands-on experience building AI agents and pipelines using LangChain and LangGraph to orchestrate LLM-based reasoning, tool usage, and decision making.
  3. Familiarity with using, fine-tuning, and evaluating popular foundation models (e.g., OpenAI, Claude, Mistral, LLaMA) for text generation, classification, and summarisation tasks.
  4. Practical experience with vector databases like Pinecone, QDrant, Weaviate, or FAISS for semantic search, retrieval-augmented generation (RAG), and embedding management.
  5. Proficiency with IaC tools such as Terraform, AWS CloudFormation, or Pulumi to automate infrastructure provisioning and deployment in cloud environments.
  6. Strong understanding of cloud-native architecture patterns and experience deploying AI workloads on AWS services such as Lambda, ECS, S3, and SageMaker.
  7. Experience designing and consuming RESTful APIs and integrating AI models into applications via APIs (e.g., OpenAI, Bedrock, Hugging Face).
  8. Knowledge of prompt design, few-shot learning, prompt templates, and optionally, fine-tuning or adapters like LoRA.
  9. Exposure to modern DevOps practices, containerisation (Docker), and CI/CD pipelines for deploying ML/AI systems.
  10. Strong analytical mindset, ability to work independently, and collaborate across teams including data scientists, engineers, and product managers.

Req 2 - Good to have

  1. Experience with deep learning frameworks like PyTorch or TensorFlow to build and train custom AI/ML models.
  2. Familiarity with ML model tracking tools such as MLflow or SageMaker Experiments for managing experiments and monitoring model performance.
  3. Understanding of model deployment at scale, including real-time inference using tools like SageMaker, TorchServe, or ONNX.
  4. Exposure to MLOps practices, including pipeline automation, model versioning, and integration with CI/CD tools for seamless deployment and monitoring.

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