Analyze product requirements and come up with data science solutions
Identify and develop data engineering scripts (example: parsers) necessary to build training datasets
Build deep learning NLP model(s), customize as needed to meet the requirements
Write production quality python code for model development and as well as for inference
Ability to think out-of-the-box and implement custom loss functions and quantitative methods to increase accuracy of AI solutions
Build and ship AI agent–driven systems that reason, plan, and act across real-world, messy data
Design agentic workflows using LLMs, tools, retrieval, and memory to solve high-impact product problems.
Work hands-on with unstructured and multimodal data (text, PDFs, drawings, images, logs).
Develop multimodal models (text + vision) for document understanding and contextual reasoning.
Own AI solutions end-to-end: data pipelines, modeling, deployment, monitoring, and iteration.
Write production-grade Python code and rapidly prototype, test, and ship in a cloud-native environment.
Take complete ownership of the solution in all phases - analysis, proof of concept, data engineering, model development, model tuning, and model implementation.
Requirements
8+ years of experience building production ML / AI systems, with recent experience in LLMs or AI agents in data science
Strong Python engineer with a bias toward clean, scalable, and maintainable code.
Proven experience working with unstructured data and images at scale.
Hands-on experience with agent patterns (tool use, function calling, planning, memory).
Good proficiency and hands-on experience with LLM's and advanced AI models
Experience deploying AI systems on AWS, GCP, or Azure in microservices architectures.
Thrives in fast-moving startup environments with high ownership and ambiguity.
Strong Python skills with focus in data engineering and data analysis. Proficiency with data mining algorithms such as Scikit-Learn, NumPy, SciPy and Pandas
Strong understanding of machine learning models, model training, and hyper parameter tuning
Working knowledge of deep learning models, loss functions, and accuracy measures
Hands-on proficiency with PyTorch / TensorFlow / Keras
Experience with statistical regression, neural nets, deep learning, decision trees, SVM, ensembles is expected
Multi-Cloud experience and proficiency with providers AWS, GCP or Azure
Comfortable working in a micro services environment
Self-motivated, enthusiasm to build next generation AI systems.
Nice to have
Experience in NLP solutions preferred
Experience in parsing pdf and text documents preferred
Additional details
Note: Please send Cv only, who can attend the interview weekdays, with short notice period (max 15 days only)
Job Location: Bangalore (work from office only) (Face to Face Interview only)
If this interests you kindly forward, your latest updated profile as reply to this mail & we will call you at your convenient time for provide more details.
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Zobhira
