Applied AI Engineer

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.
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• Participate in requirements analysis and component-level technical design.
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• Build scalable, maintainable, and high-quality software using modern programming languages and frameworks.
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• Apply AI and Agentic SSDLC practices across development, testing, deployment, and maintenance.
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• Leverage AI tools for code generation, code review, testing, debugging, documentation, and developer productivity .
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• Develop and integrate Generative AI/LLM solutions , including LLM APIs, RAG pipelines, prompt engineering, and vector-based retrieval.
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• Contribute to rapid prototyping and experimentation, including AI-assisted prototypes and proof-of-concepts.
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• Work with cloud-native architectures, microservices, FaaS/PaaS, and AI/ML services across Azure, AWS, or GCP .
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• Participate in code reviews and follow engineering standards for code quality, security, scalability, and maintainability.
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• Collaborate with product management, engineering, experience, and delivery teams to translate business and user needs into technical solutions.
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• Contribute to automated deployments and quality checks throughout the engineering lifecycle.
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• Troubleshoot technical issues and support applications in production.
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• Communicate technical decisions, progress, blockers, risks, and trade-offs clearly.
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• 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.
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• 3–5 years of software engineering experience with one or more of the following:
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• Python
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• Java
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• C# / .NET
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• Node.js
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• React / Angular
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• SQL / NoSQL
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• PyTorch / TensorFlow
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• LangChain / LangGraph
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• Unit testing frameworks
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• 1+ year of hands-on experience building AI/ML applications.
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• Practical experience with Generative AI / LLM technologies , including one or more of:
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• OpenAI
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• Anthropic
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• Open-source LLMs
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• RAG
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• Prompt engineering
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• Vector databases
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• LLM application development
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• 1+ year of cloud-native engineering experience using FaaS, PaaS, microservices, or similar architectures on Azure, AWS, or GCP .
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• Exposure to cloud AI/ML services such as:
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• Azure OpenAI
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• AWS Bedrock
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• Google Vertex AI
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• Understanding of software engineering fundamentals, including:
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• Object-Oriented Programming / Design
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• Data structures and algorithms
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• System and component design
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• Data flow and entity relationship concepts
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• Sequence, activity, and state diagrams
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• Working knowledge of modern engineering standards and best practices.
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• Ability to work effectively both independently and collaboratively.
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• 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.
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• Experience with GitHub, Azure DevOps (ADO), SonarQube, or MLflow .
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• Exposure to AI/agent observability and evaluation tools such as LangSmith, LangFuse, or equivalent .
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• Experience with AI-assisted software development and agentic engineering workflows.
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• Experience building and deploying production-grade AI applications.
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• Ability to quickly learn new technologies, frameworks, and engineering practices.
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