Sr AI Engineer (10+ yrs)

Role: Sr AI Engineer (10+ yrs)

Location: Bengaluru/ Chennai/ Hyderabad/ Pune/ Gurugram/ Mumbai (3days/week On-Site)

Job Type: Permanent

• Experience building agent orchestration or tool-calling frameworks in production
• Experience with evaluation and quality measurement for generative systems (groundedness, citation accuracy, regression testing)
• Experience integrating across heterogeneous enterprise data estates and platforms owned by other teams
• Experience in working with large datasets and distributed computing
• Experience with cloud-based AI platforms (Azure AI, AWS SageMaker, GCP AI) preferred
• Experience in enterprise or regulated environments (banking, financial services) is an advantage
• Experience handling Arabic and English content is desirable.

Required Skills:

• Machine Learning, Deep Learning, Natural Language Processing (NLP), GenAI/LLMs, Retrieval- Augmented Generation, Information Retrieval and Hybrid Search, Document Understanding and Chunking Strategies, Embeddings and Re-ranking, Knowledge Graphs, Ontologies and Semantic Layers, Agent Orchestration and Tool Calling, API and MCP Design, Python Programming, Feature Engineering, Data and Ingestion Pipelines, Prompt and Context Engineering, Model Deployment, MLOps, Evaluation and Benchmarking, Guardrails and Content Safety, Access Control and Security Trimming, Data Analysis, Statistics, Model Optimization, Model Monitoring, Observability, Hybrid and On-Premises Deployment, Containerisation and CI/CD, API Integration, Responsible AI, Explainability Techniques, Solution Design and Documentation, Problem Solving, Collaboration Preferred Tools and Certifications
• Programming: Python, SQL
• Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
• GenAI Tools: OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, Model Context Protocol (MCP)
• Retrieval and Vector: Azure AI Search, Oracle vector capabilities, open-source vector stores (for example FAISS, Milvus, Qdrant, pgvector)
• Graph: Neo4j or equivalent
• Document Processing: OCR and layout-aware parsing tooling, Arabic and English text processing
• Evaluation: RAG evaluation frameworks (for example RAGAS or equivalent) and custom golden-set harnesses
• Data Processing: Pandas, NumPy, Spark
• Data Platforms: Databricks, Snowflake
• MLOps: MLflow, Kubeflow, Azure ML; Cloud Platforms: Azure (preferred), AWS, GCP
• Deployment: Docker, Kubernetes, CI/CD, Infrastructure as Code
• Version Control: Git, Azure DevOps
• API Tools: FastAPI, Flask, OpenAPI
• Monitoring: Prometheus, Grafana, Azure Monitor Certifications