About the Role

We are looking for an AI/ML Engineer with strong expertise in Large Language Models (LLMs) and Generative AI to design, fine-tune, and deploy intelligent solutions that address real-world business problems. The ideal candidate will have hands-on experience with frameworks like LangChain, OpenAI (GPT-4), and other modern AI utilities, along with a strong foundation in machine learning, deep learning, and cloud-based AI services. This role demands a mix of technical expertise, business understanding, and communication skills to create impactful, production-ready AI solutions.

Roles & Responsibilities

Acquire and apply skills required for building, training, and deploying machine learning and deep learning models into production.
Perform feature engineering, exploratory data analysis (EDA), pipeline creation, and hyperparameter tuning on structured and unstructured datasets.
Design and develop cloud-based AI applications, including those powered by LLMs and Generative AI, and deploy them into production systems.
Collaborate with business stakeholders to translate requirements into AI-driven solutions and use cases.
Conduct performance monitoring and continuous improvement of deployed models to ensure scalability, reliability, and robustness.
Document solutions clearly and present insights using strong storytelling and communication skills.

Qualifications Required

Hands-on Experience:
- Working with LLMs (Large Language Models), LangChain, and OpenAI (GPT-4).
- Fine-tuning AI-generated prompts and using tools such as ChatGPT, Hugging Face Transformers.
Programming & ML Skills:
- Strong coding in Python.
- Familiarity with Scikit-Learn, TensorFlow, PyTorch, Keras.
- Experience in EDA, model building, hyperparameter tuning, and performance evaluation.
Applied AI/ML Knowledge:
- Deep learning algorithms, statistics (probability distributions, hypothesis testing).
- NLP, time series modeling, forecasting, image/video analytics.
MLOps & Data Engineering:
- Knowledge of data pipelines, deployment workflows, and model monitoring.
- Cloud ML services: AWS, GCP, Azure, Databricks.
Good to Have:
- Exposure to Big Data tools such as Spark, Hive.
- Experience with Databricks for data engineering and ML workflows.

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