Gen AI Engineer
- Cognizant
- 7 - 12 years
- Chennai
- 27 days ago
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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 solutionsfrom 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.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: CognizantLocation(s): Chennai
Keyskills: 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
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