Python Engineer

About Company

Our client is a Palo Alto–based AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the “Intelligent Talent Cloud,” They enabled companies to “spin up their engineering dream team in the cloud” by sourcing and managing vetted global talent.

In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deployment—powered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like Open AI, Google, Anthropic, and Meta.

Job Title: Python Engineer for Machine Learning

Location: Pan India

Experience: 3+ yrs.

Employment Type: Contract to hire

Work Mode: Remote

Notice Period: - Immediate joiner

What does day-to-day look like:

• Develop and maintain ML solutions, including data pipelines, model training, evaluation, and optimization.

• Collaborate with business stakeholders to gather and clarify requirements

• Translate business requirements into ML code that accurately reflects business logic

• Write efficient, maintainable, and well-documented ML solutions

• Participate in code reviews and follow established development standards

• Effectively analyze and select the best algorithms, optimize ML solutions for performance improvement and accuracy

• Create and maintain technical documentation for developed solutions

• Support testing activities and resolve data-related issues

Required Qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.

• 3+ years of hands-on ML development experience

• Proficiency in at least some of the ML areas and frameworks, including:

• Supervised learning (classification, regression, …)

• Unsupervised learning (clustering, anomaly detection, …)

• Time-series analysis

• Natural Language Processing (NLP)

• Computer Vision (CV)

• Statistical modeling

• Ability to understand and apply different models to real-world use cases

• Hands-on experience with DS and ML solutions in production environments

• Strong understanding of data cleaning and wrangling, feature engineering, model optimization, and evaluation metrics

• Proficiency in Python and its common data science libraries (e.g., Pandas, NumPy, Scikit-learn)