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Cognizant
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Gen AI Engineer

Gen AI Engineer

Cognizant

0 years

0 Lacs

chennai tamil nadu india

Posted:1 day ago| Platform:

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Skills Required

ai ml data learning model development deployment monitoring design governance engineering training evaluation regression inference python sql testing checks openai azure retrieval content code versioning drift controls scaling reliability programming numpy pytorch tensorflow spark databricks etl tooling mlflow airflow docker kubernetes devops github aws gcp analytics statistics probability optimization security management

Work Mode

On-site

Job Type

Full Time

Job Description

Job Summary

Returnship Hiring - Women Professionals

We are seeking an AI/ML professional with strong experience in

Data Science

Generative AI

, and

MLOps

to build end-to-end machine learning solutions—from data exploration and model development to production deployment and monitoring. The ideal candidate will design robust ML pipelines, implement GenAI solutions, and operationalize models at scale with best-in-class governance and security.

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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