| Job Description: |
Role Descriptions: Generative AI
Essential Skills: Generative AI
Desirable Skills:
Keyword:
Skills: Generative AI
Experience Required: 6-8
Hands-on experience on:
1. Programming Languages
· Strong Python familiarity (hands-on) for data prep, modeling, and building ML components.
· SQL - Skills: joins, window functions, CTEs, query optimization
2. Machine Learning
· Linear/Logistic Regression
· Decision Trees, Random Forest, XGBoost, LightGBM
· SVM, KNN
· Model evaluation - Precision/Recall, F1, ROC-AUC, MSE, RMSE
· Model tuning - Grid search, randomized search, cross-validation
3. Deep Learning
· Frameworks: TensorFlow, Keras, PyTorch
· CNNs, RNNs, LSTMs, Transformers
· Use cases: NLP, computer vision, time-series forecasting
4. Data Wrangling & Preprocessing
· Missing data handling
· Feature engineering
· Data cleaning
· Outlier detection
· Normalization/standardization 5. Data Visualization & BI Tools · Python: Matplotlib, Seaborn, Plotly · Tools: Tableau, Power BI · Dashboards, reporting, storytelling with data 6. Big Data & Cloud Tools (Needed for production-scale roles) · Big Data Frameworks: Spark, Hadoop · Cloud Platforms (any one strongly): o AWS (S3, EC2, SageMaker) o Azure (Data Factory, Databricks, ML Studio) o GCP (BigQuery, Vertex AI) 7. Deployment Skills (advanced roles) · Model deployment: Flask, FastAPI · Docker, Kubernetes (optional) · CI/CD basics 8. Databases & Data Engineering Basics · Relational: MySQL, PostgreSQL, SQL Server · NoSQL: MongoDB, Cassandra · Data pipelines: Airflow, Prefect (optional)
Roles & Responsibilities
· Define the ML use case, success metrics, and evaluation criteria; Liaise with business directly
and translate business needs into an ML approach.
· Perform data exploration, data quality checks, feature engineering, and dataset preparation
for training and testing.
· Build, train, validate, and iterate ML models; compare experiments and select the best
candidate model.
· Package the solution for production (e.g., containerized scoring/service endpoint) and support
deployment with engineering/MLOps practices
· Set up basic monitoring (model accuracy/health) and support continuous improvement
post-release. Required Skills & Experience
· Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical
experimentation.
· Experience taking models to production in a cloud-agnostic way (portable design; API/service
mindset).
· Working knowledge of version control and basic CI/CD-style collaboration with engineering
teams.
Generic Managerial Skills, If any
Key Words to search in Resume
Pre-Screening Questionnaire
*What are Regulated Positions
"Regulated Positions” are those positions which requires TAG to recruit candidates with specific work
authorizations viz., US Citizens (or) US Persons only as these may be regulated by any of the below listed
per MSA.
- ITAR (International Traffic in Arms Regulations).
- NERC CIP (NERC Critical Infrastructure Protection).
- NRC (Nuclear Regulatory Commission).
- Any other regulations as appropriate. |
