ML + GenAI Lead

Role: ML & GenAI Lead
Experience: 8–12+ years

Role Overview

The ML & GenAI Lead will be responsible for designing, leading, and delivering end-to-end Machine Learning and Generative AI solutions, with a strong focus on LLMs, Agentic AI frameworks, and production-grade ML systems. This role involves technical leadership, solution architecture, and close collaboration with business and engineering stakeholders.

Key Responsibilities

• Lead the design and implementation of ML and GenAI solutions aligned with business use cases.
• Architect and deliver LLM-based systems, including RAG, prompt engineering, fine-tuning, and agentic workflows.
• Define and drive Agentic AI architectures using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar.
• Oversee model lifecycle management: data preparation, training, evaluation, deployment, and monitoring.
• Guide teams on MLOps / LLMOps best practices including CI/CD, model versioning, observability, and cost optimization.
• Collaborate with product, data, cloud, and platform teams to ensure scalable and secure AI solutions.
• Mentor and technically guide ML and GenAI developers.
• Ensure compliance with Responsible AI, security, and governance standards.
• Engage with stakeholders to translate business problems into AI-driven solutions.

Required Skills & Qualifications

• Strong experience in Machine Learning, Deep Learning, and NLP.
• Hands-on expertise with LLMs (OpenAI, Azure OpenAI, Anthropic, Hugging Face, etc.).
• Proven experience building Agentic AI systems.
• Strong Python skills and ML frameworks: PyTorch, TensorFlow, Scikit-learn.
• Experience with RAG pipelines, vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Solid understanding of cloud platforms (Azure / AWS / GCP) and containerization (Docker, Kubernetes).
• Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Azure ML, LangSmith, etc.).
• Strong communication and leadership skills.

Role: ML & GenAI Lead
Experience: 8–12+ years

Role Overview

The ML & GenAI Lead will be responsible for designing, leading, and delivering end-to-end Machine Learning and Generative AI solutions, with a strong focus on LLMs, Agentic AI frameworks, and production-grade ML systems. This role involves technical leadership, solution architecture, and close collaboration with business and engineering stakeholders.

Key Responsibilities

• Lead the design and implementation of ML and GenAI solutions aligned with business use cases.
• Architect and deliver LLM-based systems, including RAG, prompt engineering, fine-tuning, and agentic workflows.
• Define and drive Agentic AI architectures using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar.
• Oversee model lifecycle management: data preparation, training, evaluation, deployment, and monitoring.
• Guide teams on MLOps / LLMOps best practices including CI/CD, model versioning, observability, and cost optimization.
• Collaborate with product, data, cloud, and platform teams to ensure scalable and secure AI solutions.
• Mentor and technically guide ML and GenAI developers.
• Ensure compliance with Responsible AI, security, and governance standards.
• Engage with stakeholders to translate business problems into AI-driven solutions.

Required Skills & Qualifications

• Strong experience in Machine Learning, Deep Learning, and NLP.
• Hands-on expertise with LLMs (OpenAI, Azure OpenAI, Anthropic, Hugging Face, etc.).
• Proven experience building Agentic AI systems.
• Strong Python skills and ML frameworks: PyTorch, TensorFlow, Scikit-learn.
• Experience with RAG pipelines, vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Solid understanding of cloud platforms (Azure / AWS / GCP) and containerization (Docker, Kubernetes).
• Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Azure ML, LangSmith, etc.).
• Strong communication and leadership skills.