Machine Learning Engineer - Data Modeling

<p><p><b>Key Responsibilities :</b></p><p><b><br/></b></p><p><b>LLM & Machine Learning :</b></p><p><br/></p><p>- Work with a variety of LLMs including Hugging Face OSS models, GPT (OpenAI), Gemini (Google), Claude (Anthropic), Mixtral (Mistral), and LLaMA (Meta).</p><p><br/></p><p>- Fine-tune and deploy LLMs for various use cases such as summarization, Q&A, RAG (Retrieval Augmented Generation), chatbots, document intelligence, etc.<br/><br/></p><p>- Evaluate and compare model performance and apply optimization & MLOps :</b></p><p><br/></p><p>- Design and implement complete LLMOps workflows using tools like : MLFlow for experiment tracking and model versioning.</p></p><p><br/></p><p>- LangChain, LangGraph, LangFlow for LLM orchestration.</p><p><br/></p><p>- Langfuse, LlamaIndex for observability and indexing.<br/><br/></p><p>- AWS SageMaker, Bedrock and Azure AI for model deployment and management.<br/><br/></p><p>- Monitor, log, and optimize inference latency and model behavior in & Vector Stores :</b></p><p><br/></p><p>- Work with structured and unstructured data using MongoDB and PostgreSQL.</p><p><br/></p>- Leverage vector databases like Pinecone and ChromaDB for RAG-based applications.<br/><br/></p><p>- Develop scalable data ingestion and transformation pipelines for AI training and & DevOps :</b></p><p><br/></p><p>- Deploy and manage AI workloads on AWS and Azure cloud environments.</p><p><br/></p>- Use Docker and Kubernetes for containerization and orchestration of LLM-based & Integration :</b></p><p><br/></p><p>- Build robust APIs and microservices using Python, with integrations using SQL and JavaScript where needed.</p><p><br/></p><p>- Develop UI interfaces or dashboards to visualize model outputs and system Skills :</b></p><p><br/></p><p>- Hands-on experience with multiple LLMs including GPT, Claude, Mixtral, Llama, etc.</p><p><br/></p>- Expertise in MLOps / LLMOps frameworks : MLFlow, LangChain, LangGraph, LangFlow, </p><p>Langfuse, etc.<br/><br/></p><p>- Strong understanding of cloud-native AI deployment (AWS SageMaker, Bedrock, Azure AI).<br/><br/></p><p>- Proficient in vector databases like Pinecone and ChromaDB.<br/><br/></p><p>- Familiarity with DevOps best practices using Docker and Kubernetes.<br/><br/></p><p>- Proficient in Python, SQL, and Qualifications : </b></p><p><br/></p>- Previous experience building and deploying production-grade LLM or GenAI applications.<br/><br/></p><p>- Familiarity with real-time or low-latency systems involving LLMs.<br/><br/></p><p>- Certification in AWS or Azure cloud platforms.<br/><br/></p><p>- Exposure to prompt engineering, model fine-tuning, and LLM evaluation techniques</p><br/></p> (ref:hirist.tech)