Generative AI & Machine Learning Engineer

As a GenAI Developer, you will be responsible for the end-to-end development of GenAI applications. This includes designing and building applications using LLMs such as LLaMA, Mistral, GPT-J, Falcon, and Claude. You will also implement RAG pipelines using vector databases like FAISS, ChromaDB, or Weaviate, and create and tune prompt templates, guardrails, and output parsers using tools like LangChain, Haystack, or LlamaIndex. In the realm of Agentic AI & Autonomy, you will be tasked with developing AI agents that leverage planning, memory, tools, and self-reflection. This may involve utilizing tools such as AutoGen, CrewAI, or LangGraph to build task-oriented chains with autonomous behaviors for decision-making and workflow automation. Your role will also encompass Model Training & Fine-Tuning, where you will perform SFT on open-source models using libraries like HuggingFace Transformers, PEFT, LoRA, or QLoRA. Additionally, you will evaluate model performance using GenAI metrics and frameworks such as DeepEval and TruLens. ML System Integration will be a key aspect of your responsibilities, involving the use of ML libraries like Scikit-learn, TensorFlow, PyTorch, and XGBoost to integrate traditional ML pipelines alongside GenAI components. You will be expected to develop APIs and microservices using FastAPI or Flask to expose GenAI/ML services. Furthermore, you will contribute to ensuring the security, observability, and control of AI workflows by employing red-teaming, logging, tracing, and policy controls. Additionally, you will play a role in developing tools and dashboards to explain and evaluate model behavior. The core requirements for this role include proficiency in Python, with strong coding skills and the ability to write modular, production-grade code. You should have experience in Generative AI, including using and fine-tuning LLMs, working on RAG, SFT, and prompt engineering, and being familiar with open-source LLMs and frameworks like HuggingFace and LangChain. Exposure to building multi-agent workflows using tools such as AutoGen, CrewAI, or LangGraph is also desired. In terms of ML Foundation, you should have a practical understanding of supervised/unsupervised ML models and experience using Scikit-learn, XGBoost, PyTorch, and TensorFlow. Preferred (Bonus) Skills include experience with Vector DBs like FAISS, Pinecone, ChromaDB, model evaluation tools such as DeepEval and TruLens, LLMOps tools like Weights & Biases, MLflow, BentoML, cloud services like AWS (SageMaker, Bedrock), GCP, or Azure, as well as familiarity with Docker, Git, and CI/CD for deployment.,