Machine Learning Engineer

About the Client :

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,” it 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 OpenAI, Google, Anthropic, and Meta.

Job Title : Machine Learning Engineer

Location : Pan India

Experience : 5+ yrs

Employment Type : Contract to hire

Work Mode : Remote

Notice Period : Immediate joiners

Job Description : Requirements :

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)

Preferred Qualifications :

Proven expertise in deep learning (e.g., convolutional neural networks, recurrent neural networks, transformers).

Experience with cloud data platforms (Databricks, AWS, etc.)

Knowledge of MLOps principles and tools for model deployment and monitoring

Hands-on experience with PySpark and Databricks Platform

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

Bonus : Experience and knowledge in Kaggle competitions and Benchmarks, such as MLEBench