Applied AI Engineer

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

.

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.

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.

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.