Description

Lead the technical design, development, deployment, and maintenance of AI/ML and Generative AI applications. Define engineering standards for model development, code quality, testing, documentation, model evaluation, and production deployment. Provide technical direction for ML pipelines, data processing, feature engineering, model serving, RAG systems, and AI agents. Conduct architecture reviews, design reviews, code reviews, and technical risk assessments across projects. Mentor AI/ML engineers and support their growth through technical guidance, feedback, and knowledge-sharing. Partner with product managers, architects, data teams, and business stakeholders to translate goals into technical plans. Drive decisions on frameworks, model selection, infrastructure, deployment approaches, and performance optimization. Ensure models and AI services meet requirements for quality, reliability, security, scalability, latency, and cost. Establish model monitoring, observability, experiment tracking, evaluation, retraining, and incident-response practices. Identify technical bottlenecks, manage technical debt, and create practical delivery roadmaps for the team.

Skill and Ability:

5+ years of experience in software engineering, machine learning, AI engineering, or data science. 1+ years of experience leading AI/ML technical delivery, mentoring engineers, or owning architecture decisions. Strong proficiency in Python and ML frameworks including PyTorch, TensorFlow, Scikit-learn, or Hugging Face. Experience building and deploying ML, NLP, computer vision, LLM, RAG, or agentic AI solutions in production. Solid understanding of ML lifecycle management: data preparation, training, evaluation, deployment, monitoring, and retraining. Experience with MLOps or LLMOps tools and practices, including model versioning, CI/CD, experiment tracking, and observability. Knowledge of cloud infrastructure, containers, APIs, distributed systems, databases, queues, and caching. Strong technical communication skills, with the ability to make and explain trade-offs to technical and non-technical stakeholders. Proven ability to lead code reviews, system design discussions, and technical problem-solving across delivery teams. Experience with AI agents, multi-agent systems, LLM fine-tuning, and GenAI evaluation frameworks. Familiarity with Kubernetes, Terraform, Airflow, Kubeflow, MLflow, LangSmith, or equivalent platforms. Experience working in client-facing delivery environments or across multiple concurrent AI projects. Contributions to open-source AI/ML projects, technical blogs, research, or internal engineering standards

Key Skills

Education

Bachelor's degree