Associate - Full Stack Development - IN
Full Stack Development are involved in full stack development and implement end-to-end components of custom applications. Their job involves designing and developing the UI, business logic, data & integration layer along with testing and maintenance of these layers.
Key Responsibilities and Duties
Educational Requirements
Work Experience
Physical Requirements
Career Level
6IC
Python Application Engineer
The AI-Enabled Application Engineer is a contributing software engineer on a dynamic engineering team building modern, cloud-native applications that support enterprise AI and data solutions. This role is primarily focused on backend Python development, cloud infrastructure, and building scalable, well-structured APIs and services. While deep AI research expertise is not required, the ideal candidate has working familiarity with AI-assisted development practices and a foundational understanding of how generative AI systems are built and operated.
This engineer will write clean, production-quality Python code, design reliable AWS-based architectures, and deliver software that is maintainable, secure, and performant — all while operating comfortably in an AI-forward engineering culture where tools like large language models are part of the everyday development workflow.
This is an excellent opportunity for a software engineer who has strong Python fundamentals and hands-on AWS experience, and who is eager to grow their skills in a fast-paced environment working alongside senior engineers and AI specialists.
Key Responsibilities
Backend Development and API Engineering
The engineer will design and build scalable backend services and RESTful APIs using Python and modern frameworks, with FastAPI as the primary tool of choice. This includes structuring applications for maintainability and testability, implementing proper request validation, error handling, and API versioning, and producing clear OpenAPI/Swagger documentation. The engineer is expected to write code that is clean, well-tested, and ready for production. Comfort using AI coding assistants such as Amazon Q, Copilot or similar tools to accelerate development is strongly encouraged, and the ability to critically evaluate, refine, and take ownership of AI-generated code is considered an important skill at this level.
AWS Cloud Development
A meaningful portion of this role involves building and deploying serverless and cloud-native solutions on AWS. The engineer will be expected to create and manage AWS Lambda functions for event-driven processing, work with services such as S3, DynamoDB, SQS etc. Hands-on familiarity with the AWS ecosystem is essential, and the ability to reason about cost, performance, and reliability tradeoffs is strongly valued.
AI-Adjacent Feature Development
While this role does not require deep AI research or model training expertise, the engineer will work alongside senior AI engineers to build and support components of AI-powered systems. This includes contributing to the development of backend services that power Retrieval-Augmented Generation (RAG) pipelines, integrating with vector databases such as OpenSearch to support similarity search and document retrieval, and building APIs that connect application layers to large language model (LLM) endpoints. The engineer is expected to have a working conceptual understanding of how RAG systems function — including the roles of document chunking, embedding generation, vector storage, and retrieval — even if they are not yet designing these systems end to end independently.
A basic familiarity with prompt engineering concepts is also valued. The engineer should understand how prompts are structured to guide LLM behavior, appreciate how prompt quality affects model output, and be capable of writing or modifying prompt templates as part of feature development work under the guidance of more senior team members.
Application Scalability and Performance
The engineer will build applications designed to handle growing workloads, applying patterns such as asynchronous processing, caching, and efficient data serialization. They will participate in identifying performance bottlenecks and contribute to solutions that improve throughput and reduce latency in production systems.
Testing, Quality, and DevOps Practices
The engineer is expected to write unit and integration tests as a standard part of development, not as an afterthought. They will participate in CI/CD workflows using tools such as Jenkins and ElectricFlow, contribute to code reviews, and follow team standards for branching, versioning, and deployment. Familiarity with Docker for local development and packaging is a plus, though deep Kubernetes expertise is not required at this level.
Collaboration and Communication
This engineer will work closely with senior engineers, data scientists, and product team members to translate requirements into technical solutions. They are expected to ask good questions, communicate progress and blockers clearly, and contribute constructively during design discussions and retrospectives.
Required Qualifications
Technical Skills
The ideal candidate has two to four years of software engineering experience with a strong emphasis on Python. They have built and deployed backend APIs using FastAPI or a comparable framework, understand how to structure Python projects for scalability and testability, and are comfortable working with asynchronous programming patterns. Solid experience with AWS is expected, particularly with Lambda, S3, DynamoDB, SQS, and API Gateway. The candidate should be able to independently create and deploy Lambda functions, wire them to event sources, and manage basic IAM permissions and environment configurations.
Experience with relational or NoSQL databases, including writing efficient queries and thinking about schema design, is important. Familiarity with Git-based workflows, CI/CD pipelines, and Docker is expected. Exposure to Infrastructure as Code tools such as CloudFormation or CDK is a plus.
Soft Skills
The candidate should be intellectually curious, eager to learn, and comfortable working in a team environment where priorities can shift. They should be able to receive and act on feedback from code reviews graciously, communicate their work clearly, and demonstrate a genuine interest in building software that solves real problems. An enthusiasm for AI technologies and a desire to deepen both engineering and AI skills over time is a strong indicator of success in this role.
Preferred Qualifications
Hands-on experience with AI frameworks such as LangChain, or direct integration with LLM APIs such as AWS Bedrock, OpenAI, or Anthropic Claude, is a meaningful plus. Familiarity with vector database platforms such as FAISS, or OpenSearch is valued. Experience with monitoring and observability tools such as CloudWatch or similar platforms is beneficial. AWS certifications such as the Cloud Practitioner or Developer Associate credential are viewed favorably. Prior experience in a financial services or similarly regulated industry is also a positive signal.
Growth Opportunity
This role sits within a team that builds foundational infrastructure for enterprise AI solutions, making it an excellent launchpad for engineers who want to deepen their cloud engineering skills and progressively grow into more AI-focused responsibilities. The environment is intentionally set up to support that journey — engineers at this level work alongside senior AI engineers daily, gain exposure to the full stack of generative AI system design, and are encouraged to take on stretch assignments as their skills develop. Engineers who demonstrate strong fundamentals, a collaborative mindset, and genuine curiosity about AI will have clear pathways toward senior and AI-specialized engineering roles over time.
Related SkillsAgile Methodology, Continuous Integration and Deployment, Data Analysis, Debugging, DevOps, Enterprise Application Integration, Operating Systems Management, Problem Solving, Programming, Software Development, Software Development Life Cycle, Web Application Development
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