Software Engineer (context Graph, Ai/ml)

Autodesk Autodesk · Enterprise · Pune, India

Software Engineer focused on building and operating ML model evaluation frameworks, inferencing pipelines, and AI automation workflows for Autodesk's Search & Context Graph Platform. The role involves integrating AI/ML capabilities into microservices, contributing to vector embedding pipelines and semantic search, and working with cloud infrastructure and LLM APIs.

What you'd actually do

  1. Build and maintain ML model evaluation frameworks to assess quality, relevance, and performance of AI models used in search and Context Graph systems
  2. Develop and operate model inferencing pipelines to serve AI/ML models at scale with low latency and high reliability
  3. Implement AI automation workflows to accelerate development, testing, and deployment of ML-powered features
  4. Collaborate with Search and Context Graph engineering teams to integrate AI/ML capabilities into scalable, distributed microservices
  5. Contribute to vector embedding pipelines and semantic search features that enhance relevance and user experience

Skills

Required

  • BS or MS in Computer Science, Machine Learning, or related field
  • 2-4 years of experience building and running ML systems or AI-powered applications
  • Strong machine learning fundamentals: model training, evaluation, inference, and deployment
  • Proficiency in Python for ML engineering and data pipelines
  • Experience with LLM APIs, prompt engineering, and working with foundation models
  • Hands-on experience with vector databases (e.g. Pinecone, Weaviate, pgvector) or embedding-based retrieval systems
  • Experience with Cloud infrastructure platforms such as AWS (e.g. SageMaker, Lambda, ECS)
  • Familiarity with ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers

Nice to have

  • Experience building RAG (Retrieval Augmented Generation) architectures
  • Exposure to Context Graph technologies and graph databases (e.g. Neo4j, Neptune)
  • Experience with Information Retrieval and hands-on knowledge of Lucene/ElasticSearch/OpenSearch
  • Familiarity with MLOps tools and pipelines (e.g. MLflow, Kubeflow, SageMaker Pipelines)
  • Familiarity with AI evaluation frameworks (e.g. RAGAS, LangSmith, HELM)
  • Experience in monitoring and improving reliability of AI/ML systems at scale
  • Aware of Security and Compliance challenges in Cloud AI products and platforms

What the JD emphasized

  • 2-4 years of experience building and running ML systems or AI-powered applications
  • Strong machine learning fundamentals: model training, evaluation, inference, and deployment
  • Hands-on experience with vector databases (e.g. Pinecone, Weaviate, pgvector) or embedding-based retrieval systems
  • Experience with Cloud infrastructure platforms such as AWS (e.g. SageMaker, Lambda, ECS)
  • Familiarity with AI evaluation frameworks (e.g. RAGAS, LangSmith, HELM)

Other signals

  • ML model evaluation frameworks
  • model inferencing pipelines
  • AI automation workflows
  • vector embedding pipelines
  • semantic search features