Lead Machine Learning Engineer

• Design, build, deploy, and monitor production-grade ML solutions

• Develop AI/ML applications using modern ML and GenAI frameworks

• Build and optimize end-to-end ML pipelines

• Collaborate with Product and Engineering teams to deliver business impact

• Drive best practices in MLOps, model governance, and scalability

• Design, develop, and deploy end-to-end ML solutions at scale.

• Build and optimize predictive models, recommendation systems, NLP solutions, and deep learning applications.

• Drive the complete data science lifecycle:

• Problem formulation

• Data exploration

• Feature engineering

• Model training

• Evaluation

• Build enterprise-grade GenAI applications using:

• OpenAI

• Azure OpenAI

• Anthropic Claude

• Llama

• Mistral

• Gemini

• Design and implement:

• RAG architectures

• Agentic AI systems

• Multi-agent frameworks

• Prompt engineering strategies

• Fine-tuning pipelines

• Python, SQL, Spark

• ML/DL frameworks (PyTorch, TensorFlow, Scikit-learn)

• LLMs, RAG, Agentic AI

• Docker, Kubernetes, Cloud Platforms (AWS/Azure/GCP)

• MLOps and model deployment

• Production deployment

• Excellent communication skills and ability to work with diverse stakeholders

• Experience optimizing LLMs for production (cost, latency, scaling)

• Track record of maintaining AI systems in production

• Ability to balance innovation with practical business needs

• Experience with HR/people analytics domain

Must-have skills

Machine Learning, GENAI, Production

Good-to-have skills

Gen AI