Description :

Job Description :

In This Role, Your Responsibilities Will Be :

- Design, develop, and deploy advanced ML, DL and Generative AI models (LLMs, Transformers, Diffusion, GANs, VAEs) for NLP, multimodal, forecasting, recommendation, and intelligent automation use cases.

- Lead data-driven GenAI solutioning, including problem framing, feature engineering, dataset curation and statistical validation for enterprise AI applications.

- Perform exploratory data analysis (EDA), bias detection, data quality checks, and advanced feature engineering on structured and unstructured data (text, image, tabular).

- Monitor and manage data drift, model drift, hallucination risks and performance degradation, and define retraining and recalibration strategies.

- Engineer end-to-end RAG pipelines and multi-agent workflows using LangChain, Copilot, MCP.

- Build and optimize RAG pipelines, semantic search, and contextual reasoning workflows using embeddings, chunking strategies, hybrid retrieval, and reranking techniques.

- Integrate vector DBs (PostreSQL, Pinecone, Weaviate, Chroma, FAISS, Redis) and graph DBs (Neo4j, Postgres/pgvector) for semantic retrieval and contextual reasoning.

- Optimize foundation models (GPT, LLaMA, Mistral, Falcon) via prompt engineering, RLHF, LoRA, quantization, and hyperparameter tuning.

- Build scalable AI solutions using Azure AI/ML (preferred) with containerized deployments (Docker, Kubernetes).

- Apply MLOps, LLMOps best practices : CI/CD, model versioning, drift detection, observability, and lifecycle management with MLflow, Kubeflow, Airflow, and monitoring tools.

- Develop secure AI pipelines and APIs with Python, FastAPI/Flask, RBAC, OAuth2, JWT, and encryption standards.

- Conduct model optimization : prompt engineering , hyperparameter tuning, cross-validation, and performance monitoring.

- Use tools like Azure Machine Learning , OpenAI API, HuggingFace, or custom PyTorch/TensorFlow-based models.

- Implement AI safety, bias mitigation, interpretability (SHAP, LIME, and compliance guardrails (GDPR, HIPAA, ISO).

- Collaborate with cross-functional teams to deliver enterprise-grade copilots, assistants, and reusable AI components.

- Document AI design, model workflows, and deployment pipelines for audit readiness and knowledge sharing.

For This Role, You Will Need :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field over 7+ years.

- Proven experience as a Data Scientist Developer or in a similar role and proficiency in Python.

- Experience with AI on Azure (must), including Azure OpenAI, Azure ML, and related services.

- Deep hands-on experience in Python, CUDA, SQL, proficient with TensorFlow, PyTorch, Keras.

- Familiarity with security, bias mitigation, and responsible AI frameworks.

- Experience with MLOps practices and tools for deploying, tracking, and updating models.

- Excellent problem-solving, communication, and team collaboration skills.

Preferred Qualifications That Set You Apart :

- Certifications in AI/ML from Microsoft, AWS or Coursera/edX.

- Exposure to enterprise use cases in industries such as manufacturing, finance and other.

- Experience with AutoML, LLMOps, and performance benchmarking tools.

- Understanding of semantic search, knowledge graphs, and contextual recommendation engines.

- Hands on MLOps experience, with an appreciation of the end-to-end CI/CD process.

- Certified in Azure AI Fundamentals (AI-900), Azure AI Engineer Associate (AI-102), Azure Developer Associate,.

- Experience with big data technologies.