What will your role look like

• Design, train, evaluate, and deploy traditional ML models as well as Generative AI-based applications.
• Work on supervised, unsupervised, and deep learning models including regression, classification, clustering, and sequence models.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation.
• Develop and optimize LLM-based workflows using LangChain, LangGraph, and orchestration frameworks.
• Fine-tune, evaluate, and integrate LLMs such as GPT, LLaMA, Claude, Mistral, Falcon, Cohere, and Gemini.
• Implement Retrieval-Augmented Generation (RAG) using embeddings and vector stores like FAISS, Pinecone, or Chroma.
• Apply prompt engineering, LoRA, PEFT, and adapter-based fine-tuning to optimize LLMs for specific tasks.
• Build Agentic AI systems with tool-use capabilities and reasoning chains (e.g., ReAct, AutoGPT, BabyAGI, CrewAI).
• Use Hugging Face for leveraging pre-trained models and datasets for rapid experimentation.
• Collaborate with product, data, and engineering teams to productionize AI solutions using scalable cloud infrastructure.

Why you will love this role

• Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth
• Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded.

We would like you to bring along

• Strong grasp of core ML concepts such as model selection, evaluation metrics, bias/variance tradeoff, overfitting/underfitting, etc.
• Good exposure on using Azure Open AI and hosting applications in Azure environment
• Experience in building and tuning models using scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
• Experience in building ML pipelines with feature engineering, model tuning, cross-validation, and A/B testing.
• Proficiency in LangChain, LangGraph, and integrating with Hugging Face Transformers
• Ecosystem.
• Deep knowledge of various LLMs and techniques like prompt engineering, few-shot learning, and instruction tuning.
• Experience in building Agentic AI systems and coordinating multi-agent flows or tool-chaining.
• Familiarity with LLMOps/MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI).
• Strong programming skills in Python, and experience deploying models using FastAPI, Flask, or Streamlit.
• Experience with cloud platforms (AWS/GCP/Azure) and handling GPU/TPU resources.
• Solid understanding of data structures, algorithms, and software engineering best Practices. 
• Highly skilled AI/ML Engineer with a solid foundation in Machine Learning and deep hands-on experience in Generative AI (GenAI). 
• Strong capabilities in building, training, and deploying ML models, along with significant experience.
• working with frameworks such as LangChain, LangGraph, and platforms like Hugging Face, vector databases, and various LLMs. You’ll be a key contributor in developing smart assistants, AI agents, and ML solutions that solve complex business problems.
• Experience with LangSmith, PromptLayer, or other LLM observability tools
• Familiarity with Guardrails.AI, semantic caching, and output validation techniques
• Exposure to multi-modal models like CLIP, DALL·E, Stable Diffusion, or Whisper
• Contributions to open-source GenAI or ML libraries/projects
• Domain expertise in areas like healthcare, finance, manufacturing, or legal tech.