Ai ML

What we're looking for:
At least 5 years of experience in designing & building ML/AI applications for customer
and deploying them into production
At least 8 years of Software engineering experience in building Secure, scalable and
performant applications for customers.
At least 2 years of experience leading and mentoring ML/data science teams( 4+ team
members)
Experience with Document extraction using AI, Conversational AI, Vision AI, NLP or Gen
AI.
Design, develop, and operationalize existing ML models by fine tuning, personalizing it.
Evaluate machine learning models and perform necessary tuning.
Develop prompts that instruct LLM to generate relevant and accurate responses.
Collaborate with data scientists and engineers to analyze and preprocess datasets for
prompt development, including data cleaning, transformation, and augmentation.
Conduct thorough analysis to evaluate LLM responses, iteratively modify prompts to
improve LLM performance.
Lead the end-to-end design and architecture of scalable, reliable, and cost-effective
Generative AI solutions. This includes designing RAG (Retrieval-Augmented Generation)
pipelines, agentic workflows, and model fine-tuning strategies.
Hands on customer experience with RAG solution or fine tuning of LLM model.
Build and deploy scalable machine learning pipelines on GCP or any equivalent cloud
platform involving data warehouses, machine learning platforms, dashboards or CRM
tools.
Experience working with the end-to-end steps involving but not limited to data cleaning,
exploratory data analysis, dealing outliers, handling imbalances, analyzing data
distributions (univariate, bivariate, multivariate), transforming numerical and categorical
data into features, feature selection, model selection, model training and deployment.
Act as the senior-most developer, writing clean, high-quality, and scalable code. This
includes building core components for prompt engineering, vector search, data
processing, model evaluation, and inference serving.
Proven experience building and deploying machine learning models in production
environments for real life applications
Good understanding of natural language processing, computer vision or other deep
learning techniques.
Expertise in Python, Numpy, Pandas and various ML libraries (e.g., XGboost,
TensorFlow, PyTorch, Scikit-learn, LangChain).
Familiarity with Google Cloud or any other Cloud Platform and its machine learning
services.
Excellent communication, collaboration, and problem-solving skills.
Good to Have
Google Cloud Certified Professional Machine Learning or TensorFlow Certified
Developer certifications or equivalent.
Experience of working with one or more public cloud platforms - namely GCP, AWS or
Azure.
Experience with AutoML and vision techniques.
Master’s degree in statistics, machine learning or related fields