Senior Software Engineer (context Graph , Ai/ml)

Autodesk Autodesk · Enterprise · Pune, India

Senior AI/ML Software Engineer to design and develop AI/ML systems for search and Context Graph capabilities across Autodesk's Industry Clouds. Responsibilities include leading model evaluation, inferencing infrastructure, AI automation, and mentoring engineers. Requires experience in building and running ML systems, LLM-powered applications (RAG, agents, fine-tuning), MLOps, vector databases, and cloud platforms.

What you'd actually do

  1. Design, build, and operate scalable AI/ML platforms that support search, retrieval, recommendation, and Context Graph capabilities
  2. Architect and operate scalable model inferencing pipelines to serve AI/ML models at scale with low latency, high reliability and cost efficiency
  3. Design AI automation workflows that accelerate development, testing, and deployment of ML-powered features
  4. Define technical direction and best practices for AI/ML integration into Search and Context Graph platforms
  5. Perform code reviews, adopt industry best practices and improve AI/ML development standards within the team

Skills

Required

  • BS or MS in Computer Science, Machine Learning, or related field, or equivalent work experience
  • 5-7 years of experience building and running ML systems or AI-powered applications
  • Strong machine learning fundamentals: model training, evaluation, inference, and production deployment at scale
  • Expert proficiency in Python for ML engineering; familiarity with Java or other backend languages
  • Proven experience designing and building LLM-powered applications, including RAG architectures, agents, or fine-tuning pipelines
  • Familiarity with MLOps platforms and end-to-end ML pipelines (e.g. MLflow, Kubeflow, SageMaker Pipelines)
  • Hands-on experience with vector databases (e.g. Pinecone, Weaviate, pgvector) and embedding-based retrieval systems
  • Experience with Cloud infrastructure platforms such as AWS (e.g. SageMaker, Bedrock, Lambda, ECS)
  • Experience with Information Retrieval and hands-on knowledge of Lucene/ElasticSearch/OpenSearch

Nice to have

  • Experience with Context Graph technologies and graph databases (e.g. Neo4j, Neptune, SPARQL)
  • Deep knowledge of AI evaluation benchmarks and frameworks (e.g. RAGAS, LangSmith, HELM, OpenAI Evals)
  • Experience with agentic AI systems and multi-agent orchestration frameworks (e.g. LangChain, LlamaIndex)
  • Aware of Security and Compliance challenges in Cloud AI products and platforms

What the JD emphasized

  • lead the design and development
  • drive model evaluation frameworks
  • inferencing infrastructure
  • AI automation strategies
  • shaping AI engineering practices
  • scalable AI/ML platforms
  • scalable model inferencing pipelines
  • low latency, high reliability and cost efficiency
  • AI automation workflows
  • technical direction and best practices
  • AI strategy
  • meet SLAs
  • production incidents
  • 5-7 years of experience building and running ML systems or AI-powered applications
  • model training, evaluation, inference, and production deployment at scale
  • Expert proficiency in Python for ML engineering
  • Proven experience designing and building LLM-powered applications
  • RAG architectures
  • agents
  • fine-tuning pipelines
  • vector databases
  • embedding-based retrieval systems
  • Cloud infrastructure platforms
  • Information Retrieval
  • Deep knowledge of AI evaluation benchmarks and frameworks
  • agentic AI systems
  • multi-agent orchestration frameworks

Other signals

  • design and development of AI/ML systems
  • lead the design and development
  • drive model evaluation frameworks
  • inferencing infrastructure
  • AI automation strategies
  • mentor engineers
  • shaping AI engineering practices
  • build and operate scalable AI/ML platforms
  • Architect and operate scalable model inferencing pipelines
  • low latency, high reliability and cost efficiency
  • Design AI automation workflows
  • Define technical direction and best practices for AI/ML integration
  • Partner with Principals, Software Architects, Product Managers and Engineering Managers to drive AI strategy
  • Work with SREs to meet SLAs of AI-powered services
  • Participate in on-call rotation
  • Mentor junior engineers
  • building and running ML systems or AI-powered applications
  • model training, evaluation, inference, and production deployment at scale
  • Python for ML engineering
  • designing and building LLM-powered applications
  • RAG architectures
  • agents
  • fine-tuning pipelines
  • MLOps platforms
  • end-to-end ML pipelines
  • vector databases
  • embedding-based retrieval systems
  • Cloud infrastructure platforms
  • Information Retrieval
  • Lucene/ElasticSearch/OpenSearch
  • AI evaluation benchmarks and frameworks
  • agentic AI systems
  • multi-agent orchestration frameworks