ML Lead || Hybrid || TOP MNC ||

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We are seeking a highly motivated and experienced ML Manager/Lead Data Scientist to join our growing ML/GenAI team. You will play a key role in designing, developing and productionalizing ML/GenAI applications by evaluating models, training and/or fine tuning them. As a lead, you require a deep understanding of both machine learning algorithms and software architecture. You need to take ownership of projects from conception to deployment, collaborating with engineers and stakeholders to ensure successful project delivery.

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 da

ta 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.