AI Engineer - SD1

Location: Hyderabad, Telangana, India | Experience: Junior Level (1-3 years)

About the Company

AIONOS is an AI-focused technology company building practical software and intelligent solutions for modern businesses. Our engineering teams combine machine learning, software development, data, and product thinking to solve meaningful customer problems. We value strong fundamentals, disciplined execution, curiosity, and continuous learning. As an early-career engineer, you will work in a collaborative environment where your contributions are visible, measurable, and connected to real product outcomes. You will learn from experienced teammates while helping turn research ideas and prototypes into reliable, maintainable systems. The company encourages ownership at every level, thoughtful experimentation, and clear communication across technical and business teams. We are building a team that cares about responsible AI, useful automation, and software quality. This role offers the opportunity to develop breadth across the AI engineering lifecycle while contributing to products that create tangible value for users and customers.

About the Role

As an AI Engineer at SD1 level, you will help build, test, and improve machine learning and AI-enabled software under the guidance of senior engineers. You will own well-scoped technical deliverables, convert requirements into working solutions, and use code, data, experiments, and evaluation systems to improve product reliability. The role combines software engineering with applied AI, including data preparation, model integration, prompt or model evaluation, debugging, and deployment support. You will collaborate with product, data, and engineering partners to understand user needs and deliver maintainable solutions. Success means shipping dependable components, learning quickly, documenting decisions, and improving measurable outcomes such as accuracy, latency, cost, or user experience.

Key Responsibilities

• Build and maintain AI-enabled software components using clean, testable code, helping convert product requirements into dependable features that improve user workflows and business value.
• Prepare, transform, and validate datasets through repeatable pipelines, improving data quality and creating trustworthy inputs for model training, evaluation, and production systems.
• Integrate machine learning models, APIs, and inference services into applications, balancing accuracy, latency, reliability, and operating cost through measurable engineering decisions.
• Design experiments and evaluation workflows for models or prompts, using relevant metrics and error analysis to identify improvements and communicate results clearly.
• Develop unit tests, integration tests, monitoring checks, and documentation, reducing regressions while making systems easier for teammates to operate and extend.
• Investigate defects and model-quality issues with senior engineers, tracing failures across code, data, and infrastructure to restore reliability and prevent recurrence.
• Collaborate with product and engineering stakeholders, clarifying requirements and surfacing trade-offs so delivered solutions address real customer and operational needs.

Essential Skills & Technologies

• Strong programming fundamentals in Python, with familiarity with object-oriented design, data structures, debugging, testing, Git, and writing readable production-quality code.
• Working knowledge of machine learning concepts, data preprocessing, model evaluation, and common AI workflows, with the ability to explain assumptions and interpret results.
• Familiarity with REST APIs, SQL, cloud or containerized environments, and basic software development practices for deploying, monitoring, and supporting AI-enabled applications.

Additional Plus

• Exposure to frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain, with practical understanding of when each tool supports a product requirement.
• Experience with large language models, prompt engineering, retrieval-augmented generation, vector databases, or evaluation techniques through projects, internships, or prior work.
• Familiarity with CI/CD, Docker, orchestration, observability, or cloud platforms, helping improve repeatability and operational readiness as systems move toward production.

What You'll Bring

• A strong foundation in software engineering and the motivation to apply it to artificial intelligence problems with practical product and customer outcomes.
• Evidence of learning through academic projects, internships, personal projects, open-source contributions, or professional work involving code, data, machine learning, or automation.
• A structured approach to problem-solving: you can break ambiguous requirements into smaller experiments, validate assumptions, and use evidence to choose the next action.
• Clear communication and collaborative habits, including asking thoughtful questions, sharing progress early, responding constructively to feedback, and documenting decisions for others.
• Curiosity about how AI systems behave in real environments, including their accuracy, failure modes, latency, cost, maintainability, and responsible use.
• Ownership appropriate to your level: you follow through on commitments, raise risks promptly, and seek help when needed without losing accountability for the outcome.
• Comfort working in a changing environment where priorities evolve and the best solution may require iteration, trade-offs, and close partnership across engineering and product teams.

Why Join Us

• Work on practical AI products where your engineering contributions connect directly to customer value, while gaining hands-on exposure across data, models, applications, and production systems.
• Grow through meaningful ownership, close collaboration, and feedback from experienced engineers who will help you build strong technical judgment and accelerate your development.
• Join an environment that values experimentation and quality, giving you space to learn new tools while building software that is reliable, measurable, and useful.

What We Offer

• Structured exposure to the full AI engineering lifecycle, including data, experimentation, model integration, testing, deployment, monitoring, and continuous improvement.
• A collaborative engineering environment with opportunities to learn from senior teammates, contribute to visible product outcomes, and grow through increasing responsibility.