Snr AI

Key Responsibilities 1. AI/ML Solution Development Design build and deploy machine learning and generative AI models (LLMs embeddings transformers RAG pipelines etc.). Develop scalable AI services and microservices using Python REST APIs and cloud-native technologies. Optimize models for performance accuracy and cost efficiency. 2. Data Engineering & Preparation Work with structured and unstructured datasets for feature engineering vectorization and model training. Build data pipelines for training validation and inference. Collaborate with data engineering teams on data ingestion storage and governance. 3. Model Deployment & MLOps Implement CI/CD pipelines for ML models (MLOps). Monitor model performance and drift; implement retraining strategies. Manage model lifecycle management logging and observability. 4. AI Architecture & Integration Integrate AI systems with enterprise applications APIs and cloud platforms (Azure/AWS/GCP). Build Retrieval-Augmented Generation (RAG) architectures leveraging vector databases like Pinecone FAISS Weaviate or Azure AI Search. Ensure solutions align with enterprise security compliance and ethical AI standards. 5. Cross-functional Collaboration Work with product engineering domain experts and business teams to translate requirements into technical solutions. Communicate AI capabilities and limitations to non-technical stakeholders. Conduct POCs demos and conceptual solutioning. Required Skills & Experience Technical Skills Strong proficiency in Python (NumPy Pandas PyTorch TensorFlow Transformers). Hands-on experience with LLMs (OpenAI Azure OpenAI Anthropic Llama etc.). Expertise in ML algorithms NLP deep learning and vector embeddings. Experience with cloud platforms (Azure/AWS/GCP) and serverless compute. Familiarity with MLOps tools (MLflow Kubeflow Azure ML SageMaker or Databricks). Experience using vector databases (Pinecone Chroma FAISS Azure AI Search). Knowledge of containerization (Docker Kubernetes).