Lead Machine Learning Engineer

Who we are:

Searce is a modern tech consulting firm that empowers clients to futurify their businesses, leveraging Cloud, AI & Analytics.

1. We are a category-defining niche’ cloud-native technology consulting company, specializing in modernizing (improve, automate & transform) the full-scope of infra, app, process & work.
2. We partner with clients in their ‘beyond x’ journey to drive intelligent, impactful & futuristic business outcomes.
3. We are the most preferred tech partner of choice when it comes to ‘solving for better’ for the new-age tech startups & digital enterprises, leading disruption in their industries.

What are we looking for?

As an ML Engineer, you will help develop and enhance the algorithms and technology that powers our unique system. This role covers a wide range of challenges, from developing new models using pre-existing components to enable current systems to be more intelligent.

Job Responsibilities

1. Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.
2. Managing available resources such as hardware, data, and personnel so that deadlines are met.
3. Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.
4. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
5. Verifying data quality, and/or ensuring it via data cleaning.
6. Supervising the data acquisition process if more data is needed.
7. Finding available datasets online that could be used for training.
8. Defining validation strategies.
9. Defining the preprocessing or feature engineering to be done on a given dataset.
10. Defining data augmentation pipelines.
11. Training models and tuning their hyperparameters.
12. Analyzing the errors of the model and designing strategies to overcome them.
13. Deploying models to production.
14. Knowledge and understanding of GCP Ecosystem.

Qualification

1. BE/B.Tech/Masters in a quantitative field such as CS, EE, Information sciences, Statistics, Mathematics, Economics, Operations Research, or related, with focus on applied and foundational Machine Learning , AI , NLP and/or / data-driven statistical analysis & modelling.
2. 5+ years of Experience majorly in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions to multiple domains, including financial engineering, financial processes a plus.
3. Strong, proven programming skills and with machine learning and deep learning and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop.
4. Experience with writing complex programs and implementing custom algorithms in these and other environments.
5. Experience beyond using open source tools as-is, and writing custom code on top of, or in addition to, existing open source frameworks.
6. Proven capability in demonstrating successful advanced technology solutions (either prototypes, POCs, well-cited research publications, and/or products) using ML/AI/NLP/data science in one or more domains.
7. Research and implement novel machine learning and statistical approaches.
8. Experience in data management, data analytics middleware, platforms and infrastructure, cloud and fog computing is a plus.
9. Excellent communication skills (oral and written) to explain complex algorithms, solutions to stakeholders across multiple disciplines, and ability to work in a diverse team.