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). "