Job Description
We are seeking a hands-on Applied AI Engineer with 3–5 years of software engineering experience and practical experience building AI/ML and Generative AI applications.
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In this role, you will contribute to high-impact engineering initiatives, applying modern software development practices alongside AI and agentic engineering tools across the software development lifecycle. You’ll work closely with product, engineering, and cross-functional teams to design, develop, test, deploy, and support scalable solutions that deliver measurable business and customer value.
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The ideal candidate is a strong software engineer who is curious about AI, comfortable learning new technologies, and excited to apply GenAI, LLMs, RAG, prompt engineering, and AI-enabled development practices to real-world engineering problems.
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Key Responsibilities
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- Design, develop, test, integrate, deploy, and support software components and AI-enabled applications.
- Participate in requirements analysis and component-level technical design.
- Build scalable, maintainable, and high-quality software using modern programming languages and frameworks.
- Apply AI and Agentic SSDLC practices across development, testing, deployment, and maintenance.
- Leverage AI tools for code generation, code review, testing, debugging, documentation, and developer productivity.
- Develop and integrate Generative AI/LLM solutions, including LLM APIs, RAG pipelines, prompt engineering, and vector-based retrieval.
- Contribute to rapid prototyping and experimentation, including AI-assisted prototypes and proof-of-concepts.
- Work with cloud-native architectures, microservices, FaaS/PaaS, and AI/ML services across Azure, AWS, or GCP.
- Participate in code reviews and follow engineering standards for code quality, security, scalability, and maintainability.
- Collaborate with product management, engineering, experience, and delivery teams to translate business and user needs into technical solutions.
- Contribute to automated deployments and quality checks throughout the engineering lifecycle.
- Troubleshoot technical issues and support applications in production.
- Communicate technical decisions, progress, blockers, risks, and trade-offs clearly.
- Continuously learn and adopt emerging AI, software engineering, and agentic development practices.
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Required Qualifications
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- Bachelor’s degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical discipline.
- 3–5 years of software engineering experience with one or more of the following:
- Python
- Java
- C# / .NET
- Node.js
- React / Angular
- SQL / NoSQL
- PyTorch / TensorFlow
- LangChain / LangGraph
- Unit testing frameworks
- 1+ year of hands-on experience building AI/ML applications.
- Practical experience with Generative AI / LLM technologies, including one or more of:
- OpenAI
- Anthropic
- Open-source LLMs
- RAG
- Prompt engineering
- Vector databases
- LLM application development
- 1+ year of cloud-native engineering experience using FaaS, PaaS, microservices, or similar architectures on Azure, AWS, or GCP.
- Exposure to cloud AI/ML services such as:
- Azure OpenAI
- AWS Bedrock
- Google Vertex AI
- Understanding of software engineering fundamentals, including:
- Object-Oriented Programming / Design
- Data structures and algorithms
- System and component design
- Data flow and entity relationship concepts
- Sequence, activity, and state diagrams
- Working knowledge of modern engineering standards and best practices.
- Ability to work effectively both independently and collaboratively.
- Strong written and verbal communication skills with attention to quality and detail.
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Preferred Qualifications
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- Experience with Agile / DevSecOps environments.
- Experience with GitHub, Azure DevOps (ADO), SonarQube, or MLflow.
- Exposure to AI/agent observability and evaluation tools such as LangSmith, LangFuse, or equivalent.
- Experience with AI-assisted software development and agentic engineering workflows.
- Experience building and deploying production-grade AI applications.
- Ability to quickly learn new technologies, frameworks, and engineering practices.
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