Senior Software Engineer (Context Graph , AI/ML)
Autodesk · Pune · 5–7 yrs experience · Posted 2026-07-24
Tech stack: AWS, Elasticsearch, Java, Python
Apply on the company site · Get a referral for this role
Autodesk salary & ratings · Autodesk interview process · More live openings
About the role
We are looking for a Senior AI/ML Software Engineer for the Search & Context Graph (CG) Platform within Autodesk Platform Services. In this role, you will lead the design and development of AI/ML systems that power search experiences and Context Graph capabilities across Autodesk's Industry Clouds – Fusion, Forma and Flow. You will drive model evaluation frameworks, inferencing infrastructure, and AI automation strategies while mentoring engineers and shaping AI engineering practices for the team.
Roles and Responsibilities
Design, build, and operate scalable AI/ML platforms that support search, retrieval, recommendation, and Context Graph capabilities
Architect and operate scalable model inferencing pipelines to serve AI/ML models at scale with low latency, high reliability and cost efficiency
Design AI automation workflows that accelerate development, testing, and deployment of ML-powered features
Define technical direction and best practices for AI/ML integration into Search and Context Graph platforms
Perform code reviews, adopt industry best practices and improve AI/ML development standards within the team
Partner with Principals, Software Architects, Product Managers and Engineering Managers to drive AI strategy and implement technical solutions
Work with SREs to meet SLAs of AI-powered services with monitoring, troubleshooting and optimizations
Participate in on-call rotation and help in detection, recovery and postmortems of production incidents
Mentor junior engineers and guide their growth in AI/ML engineering
Responsibilities:
- Design, build, and operate scalable AI/ML platforms that support search, retrieval, recommendation, and Context Graph capabilities
- Architect and operate scalable model inferencing pipelines to serve AI/ML models at scale with low latency, high reliability and cost efficiency
- Design AI automation workflows that accelerate development, testing, and deployment of ML-powered features
- Define technical direction and best practices for AI/ML integration into Search and Context Graph platforms
- Perform code reviews, adopt industry best practices and improve AI/ML development standards within the team
- Partner with Principals, Software Architects, Product Managers and Engineering Managers to drive AI strategy and implement technical solutions
- Work with SREs to meet SLAs of AI-powered services with monitoring, troubleshooting and optimizations
- Participate in on-call rotation and help in detection, recovery and postmortems of production incidents
- Mentor junior engineers and guide their growth in AI/ML engineering
Qualifications:
- 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,
- Experience with Information Retrieval and hands-on knowledge of Lucene/ElasticSearch/OpenSearch
- Experience with Context Graph technologies and graph databases (e.g. Neo4j, Neptune,
- Deep knowledge of AI evaluation benchmarks and frameworks (e.g.
- , 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
- Strong communicator. You enjoy synthesizing, simplifying, and articulating complex AI/ML problems to others
- A team-player, with a result-focused passion to deliver intelligent solutions that improve user experiences
- You embrace perpetual learning in the rapidly evolving AI/ML landscape and are always ready for a new challenge
- Experience with Context Graph technologies and graph databases (e.g. Neo4j, Neptune,
- Deep knowledge of AI evaluation benchmarks and frameworks (e.g.
- , 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
- Strong communicator. You enjoy synthesizing, simplifying, and articulating complex AI/ML problems to others
- A team-player, with a result-focused passion to deliver intelligent solutions that improve user experiences
- You embrace perpetual learning in the rapidly evolving AI/ML landscape and are always ready for a new challenge
- Strong communicator. You enjoy synthesizing, simplifying, and articulating complex AI/ML problems to others
- A team-player, with a result-focused passion to deliver intelligent solutions that improve user experiences
- You embrace perpetual learning in the rapidly evolving AI/ML landscape and are always ready for a new challenge
Qualifications
- 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 Experience with Information Retrieval and hands-on knowledge of Lucene/ElasticSearch/OpenSearch Experience with Context Graph technologies and graph databases (e.g. Neo4j, Neptune Deep knowledge of AI evaluation benchmarks and frameworks (e.g. 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 Strong communicator.
- You enjoy synthesizing, simplifying, and articulating complex AI/ML problems to others
- A team-player, with a result-focused passion to deliver intelligent solutions that improve user experiences
- You embrace perpetual learning in the rapidly evolving AI/ML landscape and are always ready for a new challenge
- Experience with Context Graph technologies and graph databases (e.g. Neo4j, Neptune Deep knowledge of AI evaluation benchmarks and frameworks (e.g. LangSmith, HELM, OpenAI Evals)
- You embrace perpetual learning in the rapidly evolving AI/ML landscape and are always ready for a new challenge Strong communicator.
Responsibilities
- Design, build, and operate scalable AI/ML platforms that support search, retrieval, recommendation, and Context Graph capabilities
- Architect and operate scalable model inferencing pipelines to serve AI/ML models at scale with low latency, high reliability and cost efficiency
- Design AI automation workflows that accelerate development, testing, and deployment of ML-powered features
- Define technical direction and best practices for AI/ML integration into Search and Context Graph platforms
- Perform code reviews, adopt industry best practices and improve AI/ML development standards within the team
- Partner with Principals, Software Architects, Product Managers and Engineering Managers to drive AI strategy and implement technical solutions
- Work with SREs to meet SLAs of AI-powered services with monitoring, troubleshooting and optimizations
- Participate in on-call rotation and help in detection, recovery and postmortems of production incidents
- Mentor junior engineers and guide their growth in AI/ML engineering