About the role

You will join an applied AI team building computer vision and generative AI solutions. Based on project needs and your strengths, you will work primarily in either computer vision or GenAI/RAG, with structured exposure to adjacent workflows. Senior engineers will provide architecture guidance, code review and support as you grow your delivery ownership.

What You Will Do

• Prepare, validate, and version datasets, experiment configurations, and evaluation results.
• Train, fine-tune, and evaluate models using PyTorch or TensorFlow under senior guidance; maintain reproducible experiment logs.
• Implement computer vision components such as classification, detection, segmentation, or video-processing pipelines; or build GenAI components such as prompting, chunking, embeddings, retrieval, and response generation.
• Contribute across the applied AI stack – data preparation, model experimentation, APIs, application integration, deployment, and monitoring – rather than working only on model code.
• Create proof-of-concepts to validate new models, workflows, and product ideas before wider implementation.
• Contribute reusable code, experiment findings, internal demos, and technical documentation to the internal AI Lab.
• Integrate AI components with REST APIs, vector retrieval systems, and basic tool-calling workflows.
• Write clean, testable, and documented Python; contribute through Git-based reviews and follow team engineering standards.
• Investigate model and integration defects, compare evaluation results, and communicate findings clearly.
• Collaborate with data, backend, product, and senior AI engineers to deliver scoped features from development through validation.

Required Qualifications

• 1-2 years of relevant software, data, or ML experience, or equivalent evidence through substantial academic or personal projects.
• Strong Python fundamentals, including data structures, modular code, debugging, and basic automated testing.
• Clear understanding of core software and ML concepts, including APIs, the data/model lifecycle, train/validation/test splits, overfitting, evaluation metrics, and inference.
• An open, technology-agnostic mindset, with willingness to learn and select tools based on the problem rather than preference for a particular framework.
• Working knowledge of at least one applied AI area: computer vision, GenAI/RAG, or agent/tool-calling workflows.
• Hands-on experience with at least one deep-learning framework such as PyTorch or TensorFlow.
• Familiarity with Git, REST APIs, Linux fundamentals, and Docker basics.
• Ability to evaluate results using appropriate metrics and explain the trade-offs or limitations you observe.
• Clear written and verbal communication, a learning mindset, and comfort asking for help early.

Preferred Qualifications

• Exposure to OpenCV, YOLO, Hugging Face, LangChain, LlamaIndex, or comparable libraries.
• Experience with vector search using FAISS, Qdrant, Pinecone, or an equivalent technology.
• Basic cloud or GPU-compute experience on AWS, GCP, Azure, or an on-premises environment.
• Familiarity with SQL, data pipelines, experiment tracking, or model-serving concepts.
• A portfolio, Kaggle work, research project, or deployed application demonstrating practical AI problem-solving.

Autonomy and Collaboration

You will execute well-defined tasks with regular check-ins, raise blockers early, and escalate architecture, security, and production-impacting decisions. As your judgment develops, you will take ownership of larger components and contribute more actively to technical planning.