Data Scientist Gen AI Engineer

🚀 Mega Walk-In Drive – Data Science, GenAI (5-12 Years) & MLOps Roles (3–12 Years Experience)

📍 Location: Kolkata

📅 Date: Saturday, 15th November

🕤 Time: 9:30 AM onwards

📧 Contact Person: Purbita Mondal – purbita.mondal@ltilmindtree.com

🔍 We’re Hiring For:

Data Scientists

GenAI Engineers

MLOps Engineers

Data Scientists with Gen AI:

✅ Mandatory Skills

Data Science, GenAI, Python, RAG

Cloud: Azure / AWS / GCP

AI/ML, NLP

🔸 Preferred Skills

LangChain, ChatGPT, Prompt Engineering

Vector Stores, LLaMA, PaLM, BERT, GPT, BLOOM

Deep Learning, OCR, Transformers

Regression, Forecasting, Classification

MLOps, Model Training, Deployment, Inference

CI/CD, Model Monitoring, Hyperparameter Tuning

Tools: MLflow, Kubeflow, Airflow, Docker, Kubernetes

🧠 Ideal Candidate Profile

5–12 years of experience in Data Engineering, Data Science, AI/ML, or MLOps

Strong grasp of ML algorithms: GPTs, CNN, RNN, SVM, etc.

Experience with BI tools (Power BI, Tableau), data frameworks (Hadoop, PySpark)

Familiarity with TensorFlow, PyTorch, Keras, NumPy, Pandas

Experience with cloud-native development and deployment

Hands-on with NoSQL databases (MongoDB, Cassandra, HBase, Vector DBs)

Excellent communication and analytical skills

MLOPS:

We’re looking for an MLOps Engineering Specialist who is experienced in designing and implementing ML applications at scale in production for our ML Engineering team- The team is a cross-functional team and has ML Engineers and AI Engineers, and closely works with data scientists and data engineers in designing, building and operationalizing AI/ML models-

Roles and Responsibilities:

As an ML Engineering Specialist, you will be owning responsibility to

• operationalize ML models, NLP, Computer Vision, and other type of models-

• End to End model lifecycle management, starting from feature extraction to monitor machine learning models using high end tools and technologies-

• Design & implementation of DevOps principles in Machine Learning

• Model quality assurance, governance, and monitoring

• Integrate models as part of business applications via APIs-

• Execute best practices in version control and continuous integration / delivery-

• Collaborate with data scientists, engineers, and other key stakeholders-

• Work well in a fast-paced cross-functional environment

Mandatory Skills/Requirements:

• Experience in implementing machine learning life cycle on Azure ML and Azure Databricks along with other Azure services such as Azure DevOps, Azure functions etc-

• Experience with Machine learning frameworks, libraries, and agile environments-

• Experience implementing Azure Cognitive services in business applications-

• Experience with various model deployment strategies

• Experience with Python and SQL is must- Understanding of distributed frameworks such as Spark, Dask, Ray etc- is a plus-

• Experience with version control tools such as Git, Bitbucket etc-

• Knowledge on Docker, Jenkins, Kubernetes, and other DevOps tools-

• Knowledge on Infra-as-a-Code via ARM or Terraform templates-

• Familiarity with Large Language Models and Operationalization of foundation models on cloud platforms will be a plus-

• Outstanding analytical and problem-solving skills-"

🌟 Why Join Us?

Work on cutting-edge GenAI and LLM projects

Build scalable AI/ML pipelines and deploy models in production

Collaborate with a fast-paced, innovative team

Flexible work culture and continuous learning

📩 Walk in with your resume and be part of the future of AI!

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