AI Architect
Alpharetta, GA · On-site
... developer productivity.Design and maintain "Context Environments" (indexing, .github/copilot ... compiler feedback.Implement RAG (Retrieval-Augmented Generation) systems to "talk" to legacy ...
AI Architect
Alpharetta, GA · On-site
... developer productivity.Design and maintain "Context Environments" (indexing, .github/copilot ... compiler feedback.Implement RAG (Retrieval-Augmented Generation) systems to "talk" to legacy ...
... hands-on Java engineering experience with deep expertise in JVM internals, compiler behavior ... Experience applying AI-native development practices, AI/ML or GenAI services, automation frameworks ...
... hands-on Java engineering experience with deep expertise in JVM internals, compiler behavior ... Experience applying AI-native development practices, AI/ML or GenAI services, automation frameworks ...
Ml Compiler Engineer information
What does an ML Compiler Engineer do?
An ML Compiler Engineer designs and develops compilers and software tools that optimize machine learning models for deployment on various hardware platforms. Their work involves translating high-level ML code into optimized, low-level instructions that can run efficiently on CPUs, GPUs, or specialized accelerators. They collaborate closely with hardware engineers and ML researchers to ensure models execute quickly and accurately. Additionally, ML Compiler Engineers may work on improving performance, reducing memory usage, and supporting new ML frameworks or hardware.
What are some typical collaboration points between an ML Compiler Engineer and other teams during a project?
ML Compiler Engineers frequently collaborate with machine learning researchers to understand model requirements, with hardware engineers to optimize for specific accelerators, and with software developers to ensure seamless integration into production systems. This role often involves participating in cross-functional meetings, code reviews, and design discussions to align compiler optimizations with both hardware capabilities and end-user needs. Effective communication and teamwork are essential, as these engineers play a central role in bridging the gap between algorithm design and efficient execution on target platforms.
What are the key skills and qualifications needed to thrive as an ML Compiler Engineer, and why are they important?
To thrive as an ML Compiler Engineer, you need a strong background in computer science, compiler design, machine learning concepts, and typically a degree in computer science or a related field. Familiarity with tools like LLVM, MLIR, TensorFlow XLA, and programming in C++ and Python is often required, along with experience in optimizing machine learning workloads. Strong problem-solving abilities, attention to detail, and effective collaboration skills help set top professionals apart. These skills ensure efficient translation and optimization of ML models for diverse hardware, enhancing performance and scalability.
What is the difference between Ml Compiler Engineer vs Machine Learning Engineer?
| Aspect | Ml Compiler Engineer | Machine Learning Engineer |
|---|---|---|
| Required Skills | Programming, compiler design, optimization, ML frameworks | Data analysis, model development, programming, ML frameworks |
| Work Environment | Research labs, tech companies, AI hardware firms | Tech companies, startups, data-driven organizations |
| Certifications | Computer science, software engineering, specialized compiler courses | Machine learning, data science, AI certifications |
| Industry Usage | AI hardware, software optimization, ML infrastructure | Model development, deployment, data analysis |
While both roles involve machine learning, Ml Compiler Engineers focus on optimizing ML models through compiler design and software performance, whereas Machine Learning Engineers develop and deploy ML models for applications. The roles often overlap in skills but differ in their primary focus areas.
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Job description
P1C1TSTS• Act as the team champion for GitHub Copilot and Claude Code, integrating these tools into daily workflows to increase in developer productivity.Design and maintain "Context Environments" (indexing, .github/copilot-instructions.md, etc.) to ensure AI assistants produce code that strictly adheres to our architectural standards.Master complex prompt techniques-such as Chain-of-Thought for logic extraction and Few-Shot for architectural mapping-to guide LLMs through non-trivial coding tasksDevelop and deploy AI agents (using frameworks like LangChain or LlamaIndex) to automate the conversion of legacy code (e.g., Monolithic Java, COBOL) into modern microservices.Build and maintain autonomous "migration agents" that can reason through codebase dependencies, generate pull requests, and self-heal based on compiler feedback.Implement RAG (Retrieval-Augmented Generation) systems to "talk" to legacy documentation and extract business logic for re-implementation.Establish automated testing and linting pipelines to validate all AI-generated code, ensuring security and performance benchmarks are met before human review.Create reusable prompt templates and chunking strategies to ensure consistency across different application migration workstreams.Embed Generative AI workflows directly into the CI/CD pipeline to automate documentation updates and unit test generation.5+ years of experience in AI/ML development with focus on coding assistance tools & agentic systemsHands-on development experience using Github Copilot, Claude Code for rapid AI prototyping and implementationExperience with LangGraph and agentic workflow developmentKnowledge of LLM evaluation methodologies, testing frameworks, and quality assuranceUnderstanding of reasoning models, embedding models, and prompt optimization Experience with document processing and unit test generation Experience in any of the cloud platform (AWS / Azure / GCP)
About Virtusa
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We are builders, makers, and doers with the technical skills and domain expertise to transform your business at scale and speed without disruption. Our unique Engineering First approach blends deep industry expertise and empowered, agile teams, to create holistic solutions that seamlessly move the business forward. We help clients engage with new technology paradigms to creatively build solutions that drive them to the forefront of their industries.
Industry
It services
Company size
10,000+ Employees
Headquarters location
Westborough, MA, US
Year founded
1996