- Design and deploy end-to-end ML and GenAI solutions using both traditional machine learning and LLM/Agentic technologies.
- Build scalable, production-grade AI systems while ensuring compliance with AI governance and regulatory standards.
- Collaborate with business stakeholders to define use cases, develop solutions, and create Proof of Concepts.
- Implement MLOps best practices, including model versioning, monitoring, CI/CD, and automated retraining.
- Develop and maintain data pipelines to ensure model performance, scalability, and maintainability.
- Document workflows and risk assessments while supporting responsible AI practices and audit requirements.
- Partner with cross-functional teams (CloudOps, DevOps, IT, Security) to integrate AI solutions into enterprise platforms.
The Successful Applicant
A successful candidate should have:
- Degree or Master's in AI, ML, or Data Science with proven model development experience
- 8+ years in software development, data science, and ML, including 3+ years in AI engineering roles
- Strong Python skills, knowledge of ML/AI frameworks (TensorFlow, PyTorch, scikit-learn, pandas) and LLM orchestration (Langchain, LangGraph, vLLM, LMDeploy)
- Experience with NoSQL/Graph databases, MLOps tools (MLflow, Airflow, Kubeflow), CI/CD pipelines, containerization (Docker/Kubernetes), and cloud platforms (AWS/GCP)
- Proficient in full ML lifecycle, including data wrangling, model development, deployment, monitoring, and data science techniques (supervised/unsupervised learning, NLP, time series)
- Governance & Compliance: Understanding of AI governance, model risk management, Responsible AI, bias/fairness testing, and regulatory requirements in regulated industries
- Strong problem-solving, analytical, communication, and organizational skills, with adaptability, teamwork, and respect for cultural diversity
