Associate Technical Architect - ML

Job Description

Role: Associate Technical Architect - ML

Experience Level: 5 to 8 Years

Work location: Mumbai/Bangalore

Tenure: Tenure of contract 3 Months (will extend beyond 3 months depending on client requirement)

Work timing- General day shift (unless there is any important delivery required, which will be rare)

Basic Responsibilities:

• Working with the offshore team to build, test, deploy, and assess models that predict

and optimize business outcomes based on client's success criteria.

• Work on creating high performance and scalable solution architectures for different

problems.

• Develop sophisticated yet simple interpretations and communicate insights to clients

that lead to quantifiable business impact

• Building deep relationship with clients by understanding their stated but more

importantly, latent needs.

• Working closely with the offshore delivery managers to ensure a seamless

communication and delivery cadence.

• Remain knowledgeable about current technology and carry out research to identify new

trends that can be used to achieve maximum results.

• Carry out other technical related duties that may be required. Be abreast of the best

coding and architecting practices.

Skills Required:

• Should have at least 5 years of experience with a minimum of 4 years in the design and

development of AI/ML-based systems.

• Knowledge of statistical modeling and popular machine learning models.
• Hands-on with Python/R programming and knowledge of machine learning tools like

Scikit-Learn, Pandas, Tensorflow, Pytorch, Keras, OpenCV, XG Boost is a must

• Should possess knowledge of at least one cloud platform (GCP, Azure, or AWS) and

machine learning services offered by them.

• Working knowledge of Recommender systems is a must
• Conceptual and code level understanding of Computer Vision: Image preprocessing, image

matching/comparison using SIFT/SURF/ORB, Object detection (Faster RCNN, YOLO, SSD,

etc), Image similarity (Siamese network), Image classification is a must

• NLP: Vector Space modeling in NLP, LSTMS, Sequence modeling, Attention modeling, BERT,

Transformers is good to have. Knowledge on using the above-mentioned techniques

performing Document classification, Semantic similarity, NER, Sentiment Analysis is good

to have.

• Experience in Feature engineering, Feature selection/Feature importance, Dimensionality

reduction (PCA, etc), hyperparameter tuning, Ensembling techniques like Bagging,

boosting, stacking is a must

• Experience in the design and development of continuous delivery and automation

pipelines (MLOps) in the cloud are good to have.

• Ability to use analytical thinking and business understanding to perform an insightful,

actionable quantitative and qualitative analysis of the business

• Experience in working in an Agile environment
• Strong verbal and written communication skills.