24 Aug
|
Inexture Solutions
|
Ahmedabad
24 Aug
Inexture Solutions
Ahmedabad
Job Summary
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.
Responsibilities
- 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.
- Explicit understanding of core s
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