AI / ML Solutions Engineer

Your Responsibilities

• Develop, train, and optimize ML models using PyTorch, TensorFlow, and Keras.
• Build end-to-end LLM and RAG pipelines using LangChain and LangGraph.
• Work with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and implement prompt engineering strategies.
• Utilize Hugging Face Transformers for model fine-tuning and deployment.
• Integrate embedding models for semantic search and retrieval systems.
• Work with transformer-based architectures (BERT, GPT, LLaMA, Mistral) for production use cases.
• Implement LLM evaluation frameworks (RAGAS, LangSmith) and performance optimization.
• Design and maintain Python microservices using Django or FastAPI with REST / GraphQL APIs.
• Implement real-time communication with Django Channels and FastAPI WebSockets.
• Implement pgvector for embedding storage and similarity search with efficient indexing strategies.
• Integrate vector databases (pgvector, Pinecone, Weaviate, FAISS, Milvus) for retrieval pipelines.
• Containerize AI services with Docker and deploy on Kubernetes (EKS / GKE / AKS).
• Configure AWS infrastructure (EC2, S3, RDS, SageMaker, Lambda, CloudWatch) for AI / ML workloads.
• Version ML experiments using MLflow, Weights & Biases, or Neptune.
• Deploy models using serving frameworks (TorchServe, BentoML, TensorFlow Serving).
• Implement model monitoring, drift detection, and automated retraining pipelines.
• Build CI / CD pipelines for automated testing and deployment with ≥80% test coverage (pytest).
• Follow security best practices for AI systems (prompt injection prevention, data privacy, API key management).
• Participate in code reviews, tech talks, and AI learning sessions.
• Follow Agile / Scrum methodologies and Git best practices.

Required Qualifications

Nice to Have