Senior Software Developer, AI/ML
Autodesk · Bengaluru · 5+ yrs experience · Posted 2026-07-18
Tech stack: AWS, Azure, Go, Google Cloud, Java, Python, TypeScript
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About the role
If you love building real systems that real customers use—and you get genuinely excited about LLMs, RAG, MCP, and agentic architectures—this role is for you.
The
Applied AI
team in Autodesk’s Data and Process Management (DPM) organization ships
Cloud-Native AI agents
that make our Product Data Management (PDM) and Product Lifecycle Management (PLM) workflows smarter and easier. This is a hands-on architecture role where you’ll design core platform patterns and also write code that lands in production.
We’re looking for a modern builder:
Ambitious, Curious, and Practical
—someone who has already
shipped Scalable, Cloud-Native AI Applications
to production and wants to level up further. You might have expertise in
GenAI Applications
, or you might have shipped
traditional Machine Learning applications
(recommendations, forecasting, anomaly detection) at scale and are ready to go all-in on agentic architectures. Either way, you build for reliability, quality, and impact.
AI-assisted engineering (how we deliver):
We use modern AI coding agents (e.g., Cursor Agent, Claude Code, Codex or similar) as accelerators for planning, implementation, debugging, and documentation—but you own correctness, security, and maintainability.
Responsibilities:
- Build and ship production-grade cloud-native Agentic AI applications that are
- resilient, performant, and can scale well in production
- Fine-tune, evaluate, and deploy large language models in production environments
- Implement evaluation + observability standards (regression tests, monitoring, feedback loops)
- Integrate agents with internal/external services using
- MCP-based integrations
- or equivalent tool/context integration patterns
- Partner with product managers, architects, developers, and data scientists to bring AI features to life in Autodesk products
- AI-assisted delivery: Use AI coding agents to accelerate delivery of production features and fixes, with rigorous verification (tests, CI, code review) and security-aware usage
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or equivalent practical experience
- 5+ years
- building cloud-native software in production (distributed systems, APIs, data-intensive services, reliability and operations)
- 2+ years
- of hands-on experience of delivering AI/ML-powered system in production. This can include:
- Traditional ML cloud applications
- (training pipelines, deployment, monitoring, iteration), and/or
- LLM-based systems (RAG, MCP, Agent workflows, fine-tuned models)
- Experience with
- or similar standardized patterns for connecting models to tools and context
- Experience with deploying and maintaining AI Applications in production reliably, monitoring performance, and improving over time
- Proficiency in Python/TypeScript/Java with strong engineering fundamentals (testing, code quality, performance, security)
- Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
- Demonstrated experience using AI coding tools to ship production systems
- , and the engineering judgment to verify and correct AI output (code review rigor, debugging skill, ownership of correctness)
- Experience building AI applications in the CAD or manufacturing domain
- Proven track record of building and deploying scalable cloud-native AI applications using platforms like AWS, Azure, or Google Cloud
- Familiarity with
- deep learning architectures
- (e.g., Transformers) and modern ML frameworks such as PyTorch, Lightning, and Ray
- Hands-on experience with
- SageMaker
- for scalable training and deployment
- Contributions to open-source AI projects or publications in the field
- Experience with emerging Agentic AI solutions such as LangGraph, CrewAI, A2A, Opik Comet, or equivalents.
- Experience building AI applications in the CAD or manufacturing domain
- Proven track record of building and deploying scalable cloud-native AI applications using platforms like AWS, Azure, or Google Cloud
- Familiarity with
- deep learning architectures
- (e.g., Transformers) and modern ML frameworks such as PyTorch, Lightning, and Ray
- Hands-on experience with
- SageMaker
- for scalable training and deployment
- Contributions to open-source AI projects or publications in the field
- Experience with emerging Agentic AI solutions such as LangGraph, CrewAI, A2A, Opik Comet, or equivalents.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or equivalent practical experience 5+ years
- building cloud-native software in production (distributed systems, APIs, data-intensive services, reliability and operations)
- 2+ years of hands-on experience of delivering AI/ML-powered system in production.
- This can include:
- Traditional ML cloud applications
- (training pipelines, deployment, monitoring, iteration), and/or
- LLM-based systems (RAG, MCP, Agent workflows, fine-tuned models)
- Experience with or similar standardized patterns for connecting models to tools and context
- Experience with deploying and maintaining AI Applications in production reliably, monitoring performance, and improving over time
- Proficiency in Python/TypeScript/Java with strong engineering fundamentals (testing, code quality, performance, security)
- Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
- Demonstrated experience using AI coding tools to ship production systems and the engineering judgment to verify and correct AI output (code review rigor, debugging skill, ownership of correctness)
- Experience building AI applications in the CAD or manufacturing domain
- Proven track record of building and deploying scalable cloud-native AI applications using platforms like AWS, Azure, or Google Cloud
- Familiarity with deep learning architectures
- (e.g., Transformers) and modern ML frameworks such as PyTorch, Lightning, and Ray
- Hands-on experience with SageMaker for scalable training and deployment
- Contributions to open-source AI projects or publications in the field
- Experience with emerging Agentic AI solutions such as LangGraph, CrewAI, A2A, Opik Comet, or equivalents.
Responsibilities
- Build and ship production-grade cloud-native Agentic AI applications that are resilient, performant, and can scale well in production
- Fine-tune, evaluate, and deploy large language models in production environments
- Implement evaluation + observability standards (regression tests, monitoring, feedback loops)
- Integrate agents with internal/external services using MCP-based integrations or equivalent tool/context integration patterns
- Partner with product managers, architects, developers, and data scientists to bring AI features to life in Autodesk products
- AI-assisted delivery: Use AI coding agents to accelerate delivery of production features and fixes, with rigorous verification (tests, CI, code review) and security-aware usage