ValGenesis

Life Sciences

SoftwareEngineer,AI/ML

Hyderabad, India FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Software Engineer, AI/ML at ValGenesis. Skills: AI/ML engineering, Python, Rust, ML frameworks, GenAI tools, Graph DBs, MLOps platforms, AI/ML domains. Implement, and deploy Machine Learning solutions. Develop production-grade ML models”

What You'll Achieve.

deliver real business value, ie. revenue, engagement, and customer satisfaction; improving process efficiency; enhance customer experience, content recommendation, content generation, and predictive analysis

Industry & Context.

Life Sciences
Problems you'll solve

solve complex problems

What They're Looking For.

Must Have

2 - 4 years of experience in AI/ML engineering, programming skills in Python, Rust, Experience with Pandas, NumPy, SciPy, OpenCV, Experience with ML frameworks, such as scikit-learn, Tensorflow, PyTorch, Experience with GenAI tools, such as Langchain, LlamaIndex, and open source Vector DBs, Experience with one or more Graph DBs - Neo4J, ArangoDB, Experience with MLOps platforms, such as Kubeflow or MLFlow, Expertise in one or more of the following AI/ML domains: Causal AI, Reinforcement Learning, Generative AI, NLP, Dimension Reduction, Computer Vision, Sequential Models, Expertise in building, deploying, measuring, and maintaining machine learning models to address real-world problems, Thorough understanding of software product development lifecycle, DevOps (build, continuous integration, deployment tools) and best practices, Excellent written and verbal communication skills and interpersonal skills, Advanced degree in Computer Science, Machine Learning or related field

Nice to Have

Experience in the life science domain or a related field is preferable

What You'll Do.

and deploy Machine Learning solutions

Develop production-grade ML models

Monitor and improve model performance

Stay up-to-date with the latest in machine learning and artificial intelligence

influence AI/ML for the Life science industry

How You'll Work.

Team & Collaboration

Collaborate with data product managers, software engineers and SMEs

Communication Scope

Excellent written and verbal communication skills

Full Job Description

## Description About ValGenesis  ValGenesis is a leading digital validation platform provider for life sciences companies. ValGenesis suite of products are used by 30 of the top 50 global pharmaceutical and biotech companies to achieve digital transformation, total compliance and manufacturing excellence/intelligence across their product lifecycle.    Learn more about working for ValGenesis, the de facto standard for paperless validation in Life Sciences: https://www.valgenesis.com/about About the Role: ## Responsibilities Implement, and deploy Machine Learning solutions to solve complex problems and deliver real business value, ie. revenue, engagement, and customer satisfaction. Collaborate with data product managers, software engineers and SMEs to identify AI/ML opportunities for improving process efficiency. Develop production-grade ML models to enhance customer experience, content recommendation, content generation, and predictive analysis. Monitor and improve model performance via data enhancement, feature engineering, experimentation and online/offline evaluation. Stay up-to-date with the latest in machine learning and artificial intelligence, and influence AI/ML for the Life science industry. ## Requirements 2 - 4 years of experience in AI/ML engineering, with a track record of handling increasingly complex projects. Strong programming skills in Python, Rust. Experience with Pandas, NumPy, SciPy, OpenCV (for image processing) Experience with ML frameworks, such as scikit-learn, Tensorflow, PyTorch. Experience with GenAI tools, such as Langchain, LlamaIndex, and open source Vector DBs. Experience with one or more Graph DBs - Neo4J, ArangoDB Experience with MLOps platforms, such as Kubeflow or MLFlow. Expertise in one or more of the following AI/ML domains: Causal AI, Reinforcement Learning, Generative AI, NLP, Dimension Reduction, Computer Vision, Sequential Models. Expertise in building, deploying, measuring, and maintaining machine learning models to address

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