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Cognizant
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Gen AI Engineer

Gen AI Engineer

Cognizant

7 - 12 years

27 - 42 Lacs

chennai

Posted:1 day ago| Platform:

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Skills Required

scikit-learn numpy artificial intelligence sql analytics cloud deep learning tensorflow data science spark gcp pytorch vector etl data lake ml chatbot python natural language processing microsoft azure machine learning pandas data bricks dbs science aws statistics

Work Mode

Work from Office

Job Type

Full Time

Job Description

Job Summary

Returnship Hiring - Women Professionals

Data Science

Key Responsibilities

AI/ML & Data Science

Translate business problems into ML use cases and define measurable outcomes.
Perform data exploration, feature engineering, model training, and evaluation (classification, regression, time-series, NLP, CV).
Build scalable pipelines for training and inference using Python/SQL and Spark.
Conduct A/B testing, bias/fairness checks, and explainability (SHAP/LIME).

Generative AI (GenAI)

Design and implement solutions using LLMs (e.g., OpenAI, Azure OpenAI, Cohere), embeddings, and vector databases.
Build RAG pipelines (retrieval augmented generation), prompt engineering, and evaluation frameworks.
Fine-tune or instruct-tune models; manage hallucination, toxicity, and guardrails.
Integrate GenAI into products/workflows (chatbots, content generation, code assistants).

MLOps

Operationalize ML/GenAI models with CI/CD, model versioning, and automated deployments.
Set up model monitoring (performance drift, data drift), observability, and alerting.
Implement governance: model registry, lineage, access controls, compliance.
Optimize cost/performance; manage scaling and reliability in production.

Required Skills & Qualifications

Programming:
Python (NumPy, Pandas, scikit-learn), PyTorch/TensorFlow; SQL.
ML/GenAI:
LLMs, embeddings, vector DBs (FAISS, Milvus, pgvector), RAG, prompt engineering.
Data:
Spark, Databricks, data lake/warehouse concepts; ETL/ELT.
MLOps Tooling:
MLflow, Kubeflow, Airflow, Docker, Kubernetes; CI/CD (Azure DevOps/GitHub Actions).
Cloud:
Azure (Data Factory, Synapse, Databricks, Azure ML, Azure OpenAI); experience with AWS/GCP is a plus.
Analytics/Math:
Statistics, probability, optimization, experimental design.
Security & Governance:
Role-based access, secrets management, PII handling, compliance.

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