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We are looking for an Engineer, Applied AI who is excited to work at the intersection of emerging AI technologies and real-world business impact across Collins Aerospace. In this role, you will contribute to transforming innovations in AI—such as Generative AI and agentic workflows—into practical applications that deliver measurable value. 

You will work across the development cycle—from experimenting with AI techniques to supporting deployment and participating in stakeholder demos. This is a hands-on role where you will design and build components of AI systems, working closely with senior engineers and stakeholders to deliver robust and scalable solutions. 

Key Responsibilities 

Applied AI Development: 
Design and develop components of applications leveraging AI across modalities such as text, vision, and structured data, using modern frameworks and tools. 

Prototyping & Iteration: 
Build and iterate on prototypes to validate AI concepts, support demos, and incorporate feedback to improve solutions. 

Contribution to End-to-End Delivery: 
Contribute to different stages of the AI development lifecycle—from experimentation to deployment—under guidance, ensuring quality and reliability. 

System Integration: 
Assist in developing and integrating backend services and APIs, ensuring compatibility with enterprise systems and compliance requirements. 

Automation & MLOps Support: 
Support implementation of experiment tracking, model versioning, and CI/CD workflows to help transition solutions from development to production. 

Collaboration: 
Work closely with product teams, architects, and domain experts to understand use cases, refine solutions, and contribute to delivering business value. 

Required Qualifications 

Experience: 
3-4 years of experience in software engineering or ML engineering, with at least 2 years of exposure to applied AI or data-driven application development. 

Programming: 
Proficiency in at least one object-oriented programming language, along with strong Python skills for developing AI applications, data pipelines, and backend services using frameworks such as PyTorch, FastAPI, LangChain, etc. 

Generative AI (LLMs): 
Exposure to working with LLMs (e.g., GPT, Claude, Gemini, Llama, or Mistral) for building simple workflows, experimentation, or prototypes. 

Infrastructure (Basic Exposure): 
Familiarity with tools such as Docker, Git-based workflows, and CI/CD pipelines. 

Data Handling: 
Basic understanding of relational databases (e.g., PostgreSQL), data preprocessing, and working with structured/unstructured data. 

Problem-Solving Mindset: 
Ability to work in ambiguous environments, learn quickly, and contribute to solving real-world problems using AI.