Machine Learning Engineer - LLM/GPT

Key Responsibilities :

LLM & Machine Learning :

- Work with a variety of LLMs including Hugging Face OSS models, GPT (OpenAI), Gemini (Google), Claude (Anthropic), Mixtral (Mistral), and LLaMA (Meta).

- Fine-tune and deploy LLMs for various use cases such as summarization, Q&A, RAG (Retrieval Augmented Generation), chatbots, document intelligence, etc.

- Evaluate and compare model performance and apply optimization strategies.

LLMOps & MLOps :

- Design and implement complete LLMOps workflows using tools like :

- MLFlow for experiment tracking and model versioning.

- LangChain, LangGraph, LangFlow for LLM orchestration.

- Langfuse, LlamaIndex for observability and indexing.

- AWS SageMaker, Bedrock and Azure AI for model deployment and management.

- Monitor, log, and optimize inference latency and model behavior in production.

Databases & Vector Stores :

- Work with structured and unstructured data using MongoDB and PostgreSQL.

- Leverage vector databases like Pinecone and ChromaDB for RAG-based applications.

- Develop scalable data ingestion and transformation pipelines for AI training and inference.

Cloud & DevOps :

- Deploy and manage AI workloads on AWS and Azure cloud environments.

- Use Docker and Kubernetes for containerization and orchestration of LLM-based services.

Programming & Integration :

- Build robust APIs and microservices using Python, with integrations using SQL and JavaScript where needed.

- Develop UI interfaces or dashboards to visualize model outputs and system metrics.

Essential Skills :

- Hands-on experience with multiple LLMs including GPT, Claude, Mixtral, Llama, etc.

- Expertise in MLOps / LLMOps frameworks : MLFlow, LangChain, LangGraph, LangFlow, Langfuse, etc.

- Strong understanding of cloud-native AI deployment (AWS SageMaker, Bedrock, Azure AI).

- Proficient in vector databases like Pinecone and ChromaDB.

- Familiarity with DevOps best practices using Docker and Kubernetes.

- Proficient in Python, SQL, and JavaScript.

Preferred Qualifications :

- Previous experience building and deploying production-grade LLM or GenAI applications.

- Familiarity with real-time or low-latency systems involving LLMs.

- Certification in AWS or Azure cloud platforms.

- Exposure to prompt engineering, model fine-tuning, and LLM evaluation techniques.